Web Mining : A Bird’s Eye View Sanjay Kumar Madria Department of Computer Science University of Missouri-Rolla, MO 65401 [email protected]

Web Mining : A Bird’s Eye View

Sanjay Kumar MadriaDepartment of Computer Science

University of Missouri-Rolla, MO [email protected]

Web Mining

Web mining - data mining techniques to automatically discover and extract information from Web documents/services (Etzioni, 1996).

Web mining research – integrate research from several research communities (Kosala and Blockeel, July 2000) such as: Database (DB) Information retrieval (IR) The sub-areas of machine learning (ML) Natural language processing (NLP)

WWW is huge, widely distributed, global information source for Information services:

news, advertisements, consumer information, financial management, education, government, e-

commerce, etc. Hyper-link information Access and usage information Web Site contents and Organization

Mining the World-Wide Web


Growing and changing very rapidly Broad diversity of user communities

Only a small portion of the information on the Web is truly relevant or useful to Web users

How to find high-quality Web pages on a specified topic?

WWW provides rich sources for data mining

Challenges on WWW Interactions

Finding Relevant Information Creating knowledge from Information

available Personalization of the information Learning about customers / individual

users

Web Mining can play an important Role!

Web Mining: more challenging

Searches for Web access patterns Web structures Regularity and dynamics of Web contents

Problems The “abundance” problem Limited coverage of the Web: hidden Web sources,

majority of data in DBMS Limited query interface based on keyword-oriented

search Limited customization to individual users Dynamic and semistructured

Web Mining : Subtasks

Resource Finding Task of retrieving intended web-documents

Information Selection & Pre-processing Automatic selection and pre-processing specific

information from retrieved web resources

Generalization Automatic Discovery of patterns in web sites

Analysis Validation and / or interpretation of mined patterns

Web Mining Taxonomy

Web Mining

Web Content Mining

Web Usage Mining

Web Structure

Mining

Web Content Mining

Discovery of useful information from web contents / data / documents Web data contents:

text, image, audio, video, metadata and hyperlinks. Information Retrieval View ( Structured + Semi-

Structured) Assist / Improve information finding Filtering Information to users on user profiles

Database View Model Data on the web Integrate them for more sophisticated queries

Issues in Web Content Mining

Developing intelligent tools for IR - Finding keywords and key phrases - Discovering grammatical rules and collocations

- Hypertext classification/categorization - Extracting key phrases from text documents

- Learning extraction models/rules - Hierarchical clustering - Predicting (words) relationship

Cont….

Developing Web query systems WebOQL, XML-QL

Mining multimedia data Mining image from satellite (Fayyad, et al. 1996) Mining image to identify small volcanoes on Venus

(Smyth, et al 1996) .

Web Structure Mining

To discover the link structure of the hyperlinks at the inter-document level to generate structural summary about the Website and Web page.

Direction 1: based on the hyperlinks, categorizing the Web pages and generated information.

Direction 2: discovering the structure of Web document itself.

Direction 3: discovering the nature of the hierarchy or network of hyperlinks in the Website of a particular domain.


Finding authoritative Web pages Retrieving pages that are not only relevant,

but also of high quality, or authoritative on the topic

Hyperlinks can infer the notion of authority The Web consists not only of pages, but also

of hyperlinks pointing from one page to another

These hyperlinks contain an enormous amount of latent human annotation

A hyperlink pointing to another Web page, this can be considered as the author's endorsement of the other page


Web pages categorization (Chakrabarti, et al., 1998)

Discovering micro communities on the web

- Example: Clever system (Chakrabarti, et al., 1999), Google (Brin and Page, 1998)

Schema Discovery in Semistructured Environment

Web Usage Mining

Web usage mining also known as Web log mining mining techniques to discover interesting

usage patterns from the secondary data derived from the interactions of the users while surfing the web

Web Usage Mining Applications

Target potential customers for electronic commerce

Enhance the quality and delivery of Internet information services to the end user

Improve Web server system performance Identify potential prime advertisement

locations Facilitates personalization/adaptive sites Improve site design Fraud/intrusion detection Predict user’s actions (allows prefetching)

Problems with Web Logs

Identifying users – Clients may have multiple streams

– Clients may access web from multiple hosts– Proxy servers: many clients/one address– Proxy servers: one client/many addresses

Data not in log– POST data (i.e., CGI request) not recorded– Cookie data stored elsewhere

Cont…

Missing data Pages may be cached Referring page requires client cooperation When does a session end? Use of forward and backward pointers

Typically a 30 minute timeout is used Web content may be dynamic

May not be able to reconstruct what the user saw

Use of spiders and automated agents – automatic request we pages

Cont…

Like most data mining tasks, web log mining requires preprocessing To identify users To match sessions to other data To fill in missing data Essentially, to reconstruct the click stream

Log Data - Simple Analysis

Statistical analysis of users Length of path Viewing time Number of page views

Statistical analysis of site Most common pages viewed Most common invalid URL

Web Log – Data Mining Applications

Association rules Find pages that are often viewed together

Clustering Cluster users based on browsing patterns Cluster pages based on content

Classification Relate user attributes to patterns

Web Logs

Web servers have the ability to log all requests

Web server log formats: Most use the Common Log Format (CLF) New, Extended Log Format allows configuration

of log file

Generate vast amounts of data

Remotehost: browser hostname or IP # Remote log name of user

(almost always "-" meaning "unknown")

Authuser: authenticated username Date: Date and time of the request "request”: exact request lines from

client Status: The HTTP status code returned Bytes: The content-length of response

Common Log Format

Server Logs

Fields

Client IP: 128.101.228.20 Authenticated User ID: - - Time/Date: [10/Nov/1999:10:16:39 -0600] Request: "GET / HTTP/1.0" Status: 200 Bytes: - Referrer: “-” Agent: "Mozilla/4.61 [en] (WinNT; I)"

Web Usage Mining Commonly used approaches (Borges and

Levene, 1999) - Maps the log data into relational tables before an adapted data mining technique is performed. - Uses the log data directly by utilizing special pre-processing techniques.

Typical problems - Distinguishing among unique users, server sessions, episodes, etc. in the presence of caching and proxy servers (McCallum, et al., 2000; Srivastava, et al., 2000).

Request

Method: GET

– Other common methods are POST and HEAD URI: / – This is the file that is being accessed. When a

directory is specified, it is up to the Server to

decide what to return. Usually, it will be the file

named “index.html” or “home.html” Protocol: HTTP/1.0

Status

Status codes are defined by the HTTP

protocol. Common codes include:

– 200: OK

– 3xx: Some sort of Redirection

– 4xx: Some sort of Client Error

– 5xx: Some sort of Server Error

Web Mining

Web Structure

Mining

Web ContentMining

Search ResultMining

Web PageContent Mining

General AccessPattern

Tracking

CustomizedUsage

Tracking

Web UsageMining

Web Mining Taxonomy

Web Mining

Web StructureMining

Web ContentMining

Web Page Content MiningWeb Page Summarization WebOQL(Mendelzon et.al. 1998) …:Web Structuring query languages; Can identify information within given web pages •(Etzioni et.al. 1997):Uses heuristics to distinguish personal home pages from other web pages•ShopBot (Etzioni et.al. 1997): Looks for product prices within web pages

Search ResultMining

Web UsageMining

General AccessPattern Tracking

CustomizedUsage Tracking

Mining the World Wide Web

Web Mining


Web UsageMining



Web StructureMining

Web ContentMining

Web PageContent Mining Search Result Mining

Search Engine Result Summarization•Clustering Search Result (Leouski and Croft, 1996, Zamir and Etzioni, 1997): Categorizes documents using phrases in titles and snippets

Web Mining

Web ContentMining


Search ResultMining

Web UsageMining




Web Structure Mining Using Links•PageRank (Brin et al., 1998)•CLEVER (Chakrabarti et al., 1998)Use interconnections between web pages to give weight to pages.

Using Generalization•MLDB (1994)Uses a multi-level database representation of the Web. Counters (popularity) and link lists are used for capturing structure.

Web Mining

Web StructureMining

Web ContentMining


Search ResultMining

Web UsageMining

General Access Pattern Tracking

•Web Log Mining (Zaïane, Xin and Han, 1998)Uses KDD techniques to understand general access patterns and trends.Can shed light on better structure and grouping of resource providers.



Web Mining

Web UsageMining


Customized Usage Tracking

•Adaptive Sites (Perkowitz and Etzioni, 1997)Analyzes access patterns of each user at a time.Web site restructures itself automatically by learning from user access patterns.


Web StructureMining

Web ContentMining


Search ResultMining

Web Content Mining

Agent-based Approaches: Intelligent Search Agents Information Filtering/Categorization Personalized Web Agents

Database Approaches: Multilevel Databases Web Query Systems

Intelligent Search Agents

Locating documents and services on the Web: WebCrawler, Alta Vista

(http://www.altavista.com): scan millions of Web documents and create index of words (too many irrelevant, outdated responses)

MetaCrawler: mines robot-created indices

Retrieve product information from a variety of vendor sites using only general information about the product domain: ShopBot

Intelligent Search Agents (Cont’d)

Rely either on pre-specified domain information about particular types of documents, or on hard coded models of the information sources to retrieve and interpret documents: Harvest FAQ-Finder Information Manifold OCCAM Parasite

Learn models of various information sources and translates these into its own concept hierarchy: ILA (Internet Learning Agent)

Information Filtering/Categorization

Using various information retrieval techniques and characteristics of open hypertext Web documents to automatically retrieve, filter, and categorize them. HyPursuit: uses semantic information embedded in link

structures and document content to create cluster hierarchies of hypertext documents, and structure an information space

BO (Bookmark Organizer): combines hierarchical clustering techniques and user interaction to organize a collection of Web documents based on conceptual information

Personalized Web Agents

This category of Web agents learn user preferences and discover Web information sources based on these preferences, and those of other individuals with similar interests (using collaborative filtering) WebWatcher PAINT Syskill&Webert GroupLens Firefly others

Multiple Layered Web Architecture

Generalized Descriptions

More Generalized Descriptions

Layer0

Layer1

Layern

...

Multilevel Databases

At the higher levels, meta data or generalizations are extracted from lower levels organized in structured collections, i.e. relational

or object-oriented database.

At the lowest level, semi-structured information are stored in various Web repositories, such as

hypertext documents

Multilevel Databases (Cont’d)

(Han, et. al.): use a multi-layered database where each layer

is obtained via generalization and transformation operations performed on the lower layers

(Kholsa, et. al.): propose the creation and maintenance of meta-

databases at each information providing domain and the use of a global schema for the meta-database

Multilevel Databases (Cont’d)

(King, et. al.): propose the incremental integration of a portion

of the schema from each information source, rather than relying on a global heterogeneous database schema

The ARANEUS system: extracts relevant information from hypertext

documents and integrates these into higher-level derived Web Hypertexts which are generalizations of the notion of database views

Multi-Layered Database (MLDB) A multiple layered database model

based on semi-structured data hypothesis queried by NetQL using a syntax similar to the relational

language SQL Layer-0:

An unstructured, massive, primitive, diverse global information-base.

Layer-1: A relatively structured, descriptor-like, massive,

distributed database by data analysis, transformation and generalization techniques.

Tools to be developed for descriptor extraction. Higher-layers:

Further generalization to form progressively smaller, better structured, and less remote databases for efficient browsing, retrieval, and information discovery.

Three major components in MLDB

S (a database schema): outlines the overall database structure of the global MLDB presents a route map for data and meta-data (i.e., schema)

browsing describes how the generalization is performed

H (a set of concept hierarchies): provides a set of concept hierarchies which assist the system

to generalize lower layer information to high layeres and map queries to appropriate concept layers for processing

D (a set of database relations): the whole global information base at the primitive

information level (i.e., layer-0) the generalized database relations at the nonprimitive layers

The General architecture of WebLogMiner(a Global MLDB)

Site 1

Site 2

Site 3

Generalized Data

Concept Hierarchies

Higher layers

Resource Discovery(MLDB)

Knowledge Discovery (WLM)Characteristic RulesDiscriminant RulesAssociation Rules

Techniques for Web usage mining

Construct multidimensional view on the Weblog database Perform multidimensional OLAP analysis to find the top N

users, top N accessed Web pages, most frequently accessed time periods, etc.

Perform data mining on Weblog records Find association patterns, sequential patterns, and trends

of Web accessing May need additional information,e.g., user browsing

sequences of the Web pages in the Web server buffer Conduct studies to

Analyze system performance, improve system design by Web caching, Web page prefetching, and Web page swapping

Web Usage Mining - Phases

Three distinctive phases: preprocessing, pattern discovery, and pattern analysis

Preprocessing - process to convert the raw data into the data abstraction necessary for the further applying the data mining algorithm

Resources: server-side, client-side, proxy servers, or database.

Raw data: Web usage logs, Web page descriptions, Web site topology, user registries, and questionnaire.

Conversion: Content converting, Structure converting, Usage converting

User: The principal using a client to interactively retrieve and render resources or resource manifestations.

Page view: Visual rendering of a Web page in a specific client environment at a specific point of time

Click stream: a sequential series of page view request

User session: a delimited set of user clicks (click stream) across one or more Web servers.

Server session (visit): a collection of user clicks to a single Web server during a user session.

Episode: a subset of related user clicks that occur within a user session.

Content Preprocessing - the process of converting text, image, scripts and other files into the forms that can be used by the usage mining.

Structure Preprocessing - The structure of a Website is formed by the hyperlinks between page views, the structure preprocessing can be done by parsing and reformatting the information.

Usage Preprocessing - the most difficult task in the usage mining processes, the data cleaning techniques to eliminate the impact of the irrelevant items to the analysis result.

Pattern Discovery Pattern Discovery is the key component of

the Web mining, which converges the algorithms

and techniques from data mining, machine learning, statistics and pattern recognition etc research categories.

Separate subsections: statistical analysis, association rules, clustering, classification, sequential pattern, dependency Modeling.

Statistical Analysis - the analysts may perform different kinds of descriptive statistical analyses based on different variables when analyzing the session file ; powerful tools in extracting knowledge about visitors to a Web site.

Association Rules - refers to sets of pages that are accessed together with a support value exceeding some specified threshold.

Clustering: a technique to group together users or data items (pages) with the similar characteristics. It can facilitate the development and

execution of future marketing strategies. Classification: the technique to map a data

item into one of several predefined classes, which help to establish a profile of users belonging to a particular class or category.

Pattern Analysis

Pattern Analysis - final stage of the Web usage mining.

To eliminate the irrelative rules or patterns and to extract the interesting rules or patterns from the output of the pattern discovery process.

Analysis methodologies and tools: query mechanism like SQL, OLAP, visualization

etc.

WUM – Pre-Processing Data Cleaning

Removes log entries that are not needed for the mining process

Data Integration Synchronize data from multiple server logs, metadata

User Identification Associates page references with different users

Session/Episode Identification Groups user’s page references into user sessions

Page View IdentificationPath Completion

Fills in page references missing due to browser and proxy caching

WUM – Issues in User Session Identification

A single IP address is used by many users

Different IP addresses in a single session

Missing cache hits in the server logs

different usersdifferent users Proxy Proxy serverserver Web serverWeb server

ISP serverISP server Web serverWeb serverSingle userSingle user

User and Session Identification Issues

Distinguish among different users to a site Reconstruct the activities of the users within

the site Proxy servers and anonymizers Rotating IP addresses connections through

ISPs Missing references due to caching Inability of servers to distinguish among

different visits

WUM – Solutions

Remote AgentA remote agent is implemented in Java Applet

It is loaded into the client only once when the first page is accessed

The subsequent requests are captured and send back to the server

Modified Browser The source code of the existing browser can be modified to gain user

specific data at the client side

Dynamic page rewritingWhen the user first submit the request, the server returns the requested page rewritten to include a session specific ID

Each subsequent request will supply this ID to the server

HeuristicsUse a set of assumptions to identify user sessions and find the missing cache hits in the server log

WUM – Heuristics

The session identification heuristicsTimeout: if the time between pages requests exceeds a certain limit, it is assumed that the user is starting a new sessionIP/Agent: Each different agent type for an IP address represents a different sessionsReferring page: If the referring page file for a request is not part of an open session, it is assumed that the request is coming from a different sessionSame IP-Agent/different sessions (Closest): Assigns the request to the session that is closest to the referring page at the time of the requestSame IP-Agent/different sessions (Recent): In the case where multiple sessions are same distance from a page request, assigns the request to the session with the most recent referrer access in terms of time

Cont.

The path completion heuristicsIf the referring page file of a session is not part of the previous page file of that session, the user must have accessed a cached pageThe “back” button method is used to refer a cached page Assigns a constant view time for each of the cached page file

WUM – Association Rule Generation

Discovers the correlations between pages that are most often referenced together in a single server session

Provide the informationWhat are the set of pages frequently accessed together by Web users?

What page will be fetched next?

What are paths frequently accessed by Web users?

Association rule

A B [ Support = 60%, Confidence = 80% ]

Example

“50% of visitors who accessed URLs /infor-f.html and labo/infos.html also visited situation.html”

Associations & Correlations

Page associations from usage data– User sessions– User transactions

Page associations from content data– similarity based on content analysis

Page associations based on structure– link connectivity between pages

==> Obtain frequent itemsets

Examples:

60% of clients who accessed /products/, also accessed /products/software/webminer.htm.

30% of clients who accessed /special-offer.html, placed an online order in /products/software/.

(Example from IBM official Olympics Site) {Badminton, Diving} ===> {Table Tennis}

(69.7%,.35%)

WUM – Clustering

Groups together a set of items having similar characteristics

User ClustersDiscover groups of users exhibiting similar browsing patternsPage recommendation

User’s partial session is classified into a single clusterThe links contained in this cluster are recommended

Cont..

Page clustersDiscover groups of pages having related content Usage based frequent pages Page recommendation

The links are presented based on how often URL references occur together across user sessions

Website Usage Analysis

Why developing a Website usage / utilization

analyzation tool?

Knowledge about how visitors use Website couldKnowledge about how visitors use Website could

- Prevent disorientation and help designers place

important information/functions exactly where the

visitors look for and in the way users need it

- Build up adaptive Website server

Clustering and Classification

clients who often access /products/software/webminer.html tend to be from

educational institutions.

clients who placed an online order for software tend to be students in the 20-25 age group and live in the United States.

75% of clients who download software from

/products/software/demos/ visit between 7:00 and 11:00 pm on weekends.


Discover user navigation patterns in using

Website

- Establish a aggregated log structure as a

preprocessor to reduce the search space before

the actual log mining phase

- Introduce a

model for Website usage pattern discovery by

extending the classical mining model, and

establish the processing framework of this model

Sequential Patterns & Clusters

30% of clients who visited /products/software/, had done a search in Yahoo using the keyword “software” before their visit

60% of clients who placed an online order for WEBMINER, placed another online order for software within 15 days


Website client-server architecture facilitates

recording user behaviors in every steps by

- submit client-side

log files to server when users use clear functions or

exit window/modules

The special design for local and universal

back/forward/clear functions makes user’s

navigation pattern more clear for designer by

- analyzing local back/forward history and incorporate

it with universal back/forward history


What will be included in SUA

1. Identify and collect log data

2. Transfer the data to server-side and save them in a

structure desired for analysis

3. Prepare mined data by establishing a customized

aggregated log tree/frame

4. Use modifications of the typical data mining

methods, particularly an extension of a traditional

sequence discovery algorithm, to mine user

navigation patterns


Problem need to be considered:

- How to identify the log data when a user go through

uninteresting function/module

- What marks the end of a user session?

- Client connect Website through proxy servers

Differences in Website usage analysis with common Web usage mining

- Client-side log files available

- Log file’s format (Web log files follow Common Log Format specified as a part of HTTP protocol)

- Not necessary for log file cleaning/filtering (which usually performed in preprocess of Web log mining)

Web Usage Mining - Patterns Discovery Algorithms

(Chen et. al.) Design algorithms for Path Traversal Patterns, finding maximal forward references and large reference sequences.

Path Traversal Patterns

Procedure for mining traversal patterns: (Step 1) Determine maximal forward

references from the original log data (Algorithm MF)

(Step 2) Determine large reference sequences (i.e., Lk, k1) from the set of maximal forward references (Algorithm FS and SS)

(Step 3) Determine maximal reference sequences from large reference sequences

Focus on Step 1 and 2, and devise algorithms for the efficient determination of large reference sequences

Determine large reference sequeces

Algorithm FS: Utilizes the key ideas of algorithm DHP: employs hashing and pruning techniques DHP is very efficient for the generation of candidate

itemsets, in particular for the large two-itemsets, thus greatly improving the performance bottleneck of the whole process

Algorithm SS: employs hashing and pruning techniques to reduce both

CPU and I/O costs by properly utilizing the information in candidate

references in prior passes, is able to avoid database scans in some passes, thus further reducing the disk I/O cost

Patterns Analysis Tools

WebViz [pitkwa94] --- provides appropriate tools and techniques to understand, visualize, and interpret access patterns.

Proposes OLAP techniques such as data cubes for the purpose of simplifying the analysis of usage statistics from server access logs. [dyreua et al]

Patterns Discovery and Analysis Tools

The emerging tools for user pattern discovery use sophisticated techniques from AI, data mining, psychology, and information theory, to mine for knowledge from collected data: (Pirolli et. al.) use information foraging theory to

combine path traversal patterns, Web page typing, and site topology information to categorize pages for easier access by users.

(Cont’d)

WEBMINER : introduces a general architecture for Web usage

mining, automatically discovering association rules and sequential patterns from server access logs.

proposes an SQL-like query mechanism for querying the discovered knowledge in the form of association rules and sequential patterns.

WebLogMiner Web log is filtered to generate a relational database Data mining on web log data cube and web log

database

WEBMINER

SQL-like Query A framework for Web mining, the

applications of data mining and knowledge discovery techniques, association rules and sequential patterns, to Web data: Association rules: using apriori algorithm

40% of clients who accessed the Web page with URL /company/products/product1.html, also accessed /company/products/product2.html

Sequential patterns: using modified apriori algorithm 60% of clients who placed an online order in

/company/products/product1.html, also placed an online order in /company/products/product4.html within 15 days

WebLogMiner

Database construction from server log file: data cleaning data transformation

Multi-dimensional web log data cube construction and manipulation

Data mining on web log data cube and web log database


Design of a Web Log Miner Web log is filtered to generate a relational database A data cube is generated form database OLAP is used to drill-down and roll-up in the cube OLAM is used for mining interesting knowledge

G)p,q( )q(reedegout

)q(R)1(n/)p(R

1Data Cleaning

2Data CubeCreation

3OLAP

4Data Mining

Web log Database Data Cube Sliced and dicedcube

Knowledge

Construction of Data Cubes(http://db.cs.sfu.ca/sections/publication/slides/slides.html)

sum

0-20K20-40K 60K- sum

Comp_Method

… ...

sum

Database

Amount

Province

Discipline

40-60KB.C.

PrairiesOntario

All AmountComp_Method, B.C.

Each dimension contains a hierarchy of values for one attributeA cube cell stores aggregate values, e.g., count, sum, max, etc.A “sum” cell stores dimension summation values.Sparse-cube technology and MOLAP/ROLAP integration.“Chunk”-based multi-way aggregation and single-pass computation.

WebLogMiner Architecture

Web log is filtered to generate a relational database

A data cube is generated from database OLAP is used to drill-down and roll-up in

the cube OLAM is used for mining interesting

knowledge

G)p,q( )q(reedegout

)q(R)1(n/)p(R

1Data Cleaning

2Data CubeCreation

3OLAP

4Data Mining

Web logDatabase

Data Cube Sliced and dicedcube

Knowledge

HITS Algorithm--Topic Distillation on WWW

HITS Method

Hyperlink Induced Topic Search Kleinberg, 1998 A simple approach by finding hubs and authorities View web as a directed graph Assumption: if document A has hyperlink to

document B, then the author of document A thinks that document B contains valuable information

Main Ideas

Concerned with the identification of the most authoritative, or definitive, Web pages on a broad-topic

Focused on only one topic

Viewing the Web as a graph

A purely link structure-based computation, ignoring the textual content

HITS: Hubs and Authority

Hub: web page links to a collection of prominent sites on a common topic

Authority: Pages that link to a collection of authoritative pages on a broad topic; web page pointed to by hubs

Mutual Reinforcing Relationship: a good authority is a page that is pointed to by many good hubs, while a good hub is a page that points to many good authorities

Hub-Authority Relations

Hubs Authorities

Unrelated page of large in-degree

HITS: Two Main Steps

A sampling component, which constructs a focused collection of several thousand web pages likely to be rich in relevant authorities

A weight-propagation component, which determines numerical estimates of hub and authority weights by an iterative procedure

As the result, pages with highest weights are returned as hubs and authorities for the research topic

HITS: Root Set and Base Set

Using query term to collect a root set (S) of pages from index-based search engine (AltaVista)

Expand root set to base set (T) by including all pages linked to by pages in root set and all pages that link to a page in root set (up to a designated size cut-off)

Typical base set contains roughly 1000-5000 pages

Step 1: Constructing Subgraph

1.1 Creating a root set (S) - Given a query string on a broad topic - Collect the t highest-ranked pages for the query from a text-based search engine

1.2 Expanding to a base set (T) - Add the page pointing to a page in root set - Add the page pointed to by a page in root set

Root Set and Base Set (Cont’d)

ST

S

Step 2: Computing Hubs and Authorities

2.1 Associating weights

- Authority weight xp

- Hub weight yp

- Set all values to a uniform constant initially

2.2 Updating weights

Updating Authority Weight

q such that qpxp = yq

P

q1

q3

q2

xp=yq1+yq2+yq3

Example

Updating Hub Weight

yp = xqq such that pq

Example

yp=xq1+xq2+xq3

P

q1

q3

q2

Flowchart

Updateall x-

values

Initialization Set all values to c, e.g. c =1

Updateall y-

values

Updateall y-

values

Updateall x-

values

1st time

2nd time

Results

All x- and y-values converge rapidly so that termination of the iteration is guaranteed

It can be proved in mathematical approach

Pages with the highest x-values are viewed as the best authorities, while pages with the highest y-values are regarded as the best hubs

Implementation

Search engine: AltaVista Root set: 200 pages Base set: 1000-5000

pages Converging speed: Very rapid, less than 20

times Running time: About 30

minutes

HITS: Advantages

Weight computation is an intrinsic feature from collection of linked pages

Provides a densely linked community of related authorities and hubs

Pure link-based computation once the root set has been assembled, with no further regard to query terms

Provides surprisingly good search result for a wide range of queries

Drawbacks

Limit On Narrow Topics Not enough authoritative pages Frequently returns resources for a

more general topic adding a few edges can potentially

change scores considerably

Topic Drifting- Appear when hubs discuss multiple

topics

To improve precision:- Combining content with link information- Breaking large hub pages into smaller units- Computing relevance weights for pages

To improve speed: - Building a Connectivity Server that provides

linkage information for all pages

Improved Work


Page-Rank Method CLEVER Method Connectivity-Server Method

1. Page-Rank Method

Introduced by Brin and Page (1998) Mine hyperlink structure of web to produce ‘global’

importance ranking of every web page Used in Google Search Engine Web search result is returned in the rank order Treats link as like academic citation Assumption: Highly linked pages are more ‘important’ than

pages with a few links A page has a high rank if the sum of the ranks of its back-

links is high

Page Rank: Computation

Assume: R(u) : Rank of a web page u Fu : Set of pages which u points to

Bu : Set of pages that points to u

Nu : Number of links from u C : Normalization factor E(u) : Vector of web pages as source of rank

Page Rank Computation:

)()(

)( ucEN

vRcuR

uBv v

Page Rank: Implementation

Stanford WebBase project Complete crawling and indexing system of with current repository 24 million web pages (old data)

Store each URL as unique integer and each hyperlink as integer IDs

Remove dangling links by iterative procedures Make initial assignment of the ranks Propagate page ranks in iterative manner Upon convergence, add the dangling links back and

recompute the rankings

Page Rank: Results

Google utilizes a number of factors to rank the search results: proximity, anchor text, page rank

The benefits of Page Rank are the greatest for underspecified queries, example: ‘Stanford University’ query using Page Rank lists the university home page the first

Page Rank: Advantages

Global ranking of all web pages – regardless of their content, based solely on their location in web graph structure

Higher quality search results – central, important, and authoritative web pages are given preference

Help find representative pages to display for a cluster center

Other applications: traffic estimation, back-link predictor, user navigation, personalized page rank

Mining structure of web graph is very useful for various information retrieval

CLEVER Method

CLient–side EigenVector-Enhanced Retrieval Developed by a team of IBM researchers at IBM

Almaden Research Centre Continued refinements of HITS Ranks pages primarily by measuring links between

them Basic Principles – Authorities, Hubs

Good hubs points to good authorities Good authorities are referenced by good hubs

Problems Prior to CLEVER

Textual content that is ignored leads to problems caused by some features of web: HITS returns good resources for more general topic when

query topics are narrowly-focused HITS occasionally drifts when hubs discuss multiple topics Usually pages from single Web site take over a topic and

often use same html template therefore pointing to a single popular site irrelevant to query topic

CLEVER: Solution

Replacing the sums of Equation (1) and (2) of HITS with weighted sums

Assign to each link a non-negative weight Weight depends on the query term and end point Extension 1: Anchor Text

using text that surrounds hyperlink definitions (href’s) in Web pages, often referred as ‘anchor text’

boost weight enhancements of links that occur near instances of query terms

CLEVER: Solution (Cont’d)

Extension 2: Mini Hub Pagelets breaking large hub into smaller units treat contiguous subsets of links as mini-hubs or

‘pagelets’ contiguous sets of links on a hub page are more

focused on single topic than the entire page

CLEVER: The Process

Starts by collecting a set of pages Gathers all pages of initial link, plus any pages

linking to them Ranks result by counting links Links have noise, not clear which pages are best Recalculate scores Pages with most links are established as most

important, links transmit more weigh Repeat calculation no. of times till scores are

refined

CLEVER: Advantages

Used to populate categories of different subjects with minimal human assistance

Able to leverage links to fill category with best pages on web

Can be used to compile large taxonomies of topics automatically

Emerging new directions: Hypertext classification, focused crawling, mining communities

Connectivity Server Method Server that provides linkage information

for all pages indexed by a search engine In its base operation, server accepts a

query consisting of a set of one or more URLs and return a list of all pages that point to pages in (parents) and list of all pages that are pointed to from pages in (children)

In its base operation, it also provides neighbourhood graph for query set

Acts as underlying infrastructure, supports search engine applications

What’s Connectivity Server (Cont’d)

Neighborhood GraphNeighborhood Graph

CONSERV: Web Structure Mining

Finding Authoritative Pages (Search by topic)(pages that is high in quality and relevant to the

topic)

Finding Related Pages (Search by URL)(pages that address same topic as the original

page, not necessarily semantically identical)Algorithms include Companion, Cocitation

CONSERV: Finding Related Page

CONSERV: Companion Algorithm

An extension to HITS algorithmFeatures: Exploit not only links but also their order on a page Use link weights to reduce the influence of pages

that all reside on one host Merge nodes that have a large number of duplicate

links The base graph is structured to exclude

grandparent nodes but include nodes that share child

Companion Algorithm (Cont’d)

Four steps1. Build a vicinity graph for u2. Remove duplicates and near-duplicates in

graph.3. Compute link weights based on host to host

connection4. Compute a hub score and a authority score

for each node in the graph, return the top ranked authority nodes.

Companion Algorithm (Cont’d)Building the Vicinity Graph

Set up parameters: B : no of parents of u, BF : no of children per parent, F : no of children of u, FB : no of parents per child

Stoplist (pages that are unrelated to most queries and have a very high in-degree)

ProcedureGo Back (B) : choose parents (randomly)Back-Forward(BF) : choose siblings (nearest)Go Forward (F) : choose children (first) Forward-Back(FB) : choose siblings (highest

in-degree)

Companion Algorithm (Cont’d)Remove duplicate

Near-duplicate, if two nodes, each has more than 10 links and they have at least 95% of their links in common

Replace two nodes with a node whose links are the union of the links of the two nodes

(mirror sites, aliases)

Companion Algorithm (Cont’d)Assign edge (link) weights

Link on the same host has weight 0 If there are K links from documents on a

host to a single document on diff host, each link has an authority weight of 1/k

If there are k links from a single document on a host to a set of documents on diff host, give each link a hub weight of 1/k

(prevent a single host from having too much influence on the computation)

Companion Algorithm (Cont’d)Compute hub and authority scores

Extension of the HITS algorithm with edge weights Initialize all elements of the hub vector H to 1 Initialze all elements of the authority vector A to 1 While the vectors H and A have not converged: For all nodes n in the vicinity graph N, A[n] := (n',n)edges(N) H[n'] x

authority_weight(n',n) For all n in N, H[n] := (n',n)edges(N) A[n'] x hub_weight(n',n) Normalize the H and A vectors.

CONSERV: Cocitation Algorithm

Two nodes are co-cited if they have a common parent

The number of common parents of two nodes is their degree of co-citation

Determine the related pages by looking for sibling nodes with the highest degree of co-citation

In some cases there is an insufficient level of cocitation to provide meaningful results, chop off elements of URL, restart algorithm.e.g. A.com/X/Y/Z A.com/X/Y

Comparative Study

Page Rank (Google) Assigns initial ranking

and retains them independently from queries (fast)

In the forward direction from link to link

Qualitative result

Hub/Authority (CLEVER, C-Server) Assembles different root

set and prioritizes pages in the context of query

Looks forward and backward direction

Qualitative result

Connectivity-Based Ranking

Query-independent: gives an intrinsic quality score to a page

Approach #1: larger number of hyperlinks pointing to a page, the better the page drawback? each link is equally important

Approach #2: weight each hyperlink proportionally to the quality of the page containing the hyperlink

Query-dependent Connectivity-Based Ranking

Carrier and Kazman For each query, build a subgraph of the

link graph G limited to pages on query topic

Build the neighborhood graph1. A start set S of documents matching query

given by search engine (~200)2. Set augmented by its neighborhood, the set of

documents that either point to or are pointed to by documents in S (limit to ~50)

3. Then rank based on indegree

Idea

We desire pages that are relevant (in the neighborhood graph) and authoritative

As in page rank, not only the in-degree of a page p, but the quality of the pages that point to p. If more important pages point to p, that means p is more authoritative

Key idea: Good hub pages have links to good authority pages

given user query, compute a hub score and an authority score for each document

high authority score relevant content high hub score links to documents with

relevant content

Improvements to Basic Algorithm

Put weights on edges to reflect importance of links, e.g., put higher weight if anchor text associated with the link is relevant to query

Normalize weights outgoing from a single source or coming into a single sink. This alleviates spamming of query results

Eliminate edges between same domain

Discovering Web communities on the web

Introduction

Introduction of the cyber-community

Methods to measure the similarity of web pages on the web graph

Methods to extract the meaningful communities through the link structure

What is cyber-community

A community on the web is a group of web pages sharing a common interest Eg. A group of web pages talking about POP

Music Eg. A group of web pages interested in data-

mining Main properties:

Pages in the same community should be similar to each other in contents

The pages in one community should differ from the pages in another community

Similar to cluster

Two different types of communities

Explicitly-defined communities They are well known ones,

such as the resource listed by Yahoo!

Implicitly-defined communities They are communities

unexpected or invisible to most users

Arts

Music

Classic Pop

Painting

eg.

eg. The group of web pages interested in a particular singer

Two different types of communities

The explicit communities are easy to identify Eg. Yahoo!, InfoSeek, Clever System

In order to extract the implicit communities, we need analyze the web-graph objectively

In research, people are more interested in the implicit communities

Similarity of web pages

Discovering web communities is similar to clustering. For clustering, we must define the similarity of two nodes

A Method I: For page and page B, A is related to B if there is a

hyper-link from A to B, or from B to A

Not so good. Consider the home page of IBM and Microsoft.

Page A

Page B

Similarity of web pages

Method II (from Bibliometrics) Co-citation: the similarity of A and B is

measured by the number of pages cite both A and B

Bibliographic coupling: the similarity of A and B is measured by the number of pages cited by both A and B.

Page A Page B

Page A Page B

Methods of clustering

Clustering methods based on co-citation analysis:

Methods derived from HITS (Kleinberg) Using co-citation matrix

All of them can discover meaningful communitiesBut their methods are very expensive to the whole World Wide Web with billions of web pages.

A cheaper method

The method from Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew Tomkins IBM Almaden Research Center

They call their method communities trawling (CT)

They implemented it on the graph of 200 millions pages, it worked very well

Basic idea of CT

Definition of communities

dense directed bipartite sub graphs Bipartite graph: Nodes

are partitioned into two sets, F and C

Every directed edge in the graph is directed from a node u in F to a node v in C

dense if many of the possible edges between F and C are present

Fans Centers

F C

Basic idea of CT

Bipartite cores a complete bipartite

subgraph with at least i nodes from F and at least j nodes from C

i and j are tunable parameters

A (i, j) Bipartite core

Every community have such a core with a certain i and j.

A (i=3, j=3) bipartite core

Basic idea of CT

A bipartite core is the identity of a community

To extract all the communities is to enumerate all the bipartite cores on the web.

Author invent an efficient algorithm to enumerate the bipartite cores. Its main idea is iterate pruning -- elimination-generation pruning

• Complete bipartite graph: there is an edge between each node in F and each node in C

• (i,j)-Core: a complete bipartite graph with at least i nodes in F and j nodes in C

• (i,j)-Core is a good signature for finding online communities

•“Trawling”: finding cores

• Find all (i,j)-cores in the Web graph.

– In particular: find “fans” (or “hubs”) in the graph

– “centers” = “authorities”

– Challenge: Web is huge. How to find cores efficiently?

Main idea: pruning

• Step 1: using out-degrees

– Rule: each fan must point to at least 6 different websites

– Pruning results: 12% of all pages (= 24M pages) are potential fans

– Retain only links, and ignore page contents

Many pages are mirrors (exactly the same page)

They can produce many spurious fans Use a “shingling” method to identify

and eliminate duplicates Results: – 60% of 24M potential-fan pages are

removed – # of potential centers is 30 times of #

of potential fans

Step 2: Eliminate mirroring pages

Step 4: Iterative pruning

To find (i,j)-cores– Remove all pages whose # of out-links is < i– Remove all pages whose # of in-links is < j– Do it iteratively

Step 5: inclusion-exclusion pruning Idea: in each step, we – Either “include” a community” – Or we “exclude” a page from further contention

Check a page x with j out-degree. x is a fan of a (i,j)-core if:

– There are i-1 fans point to all the forward neighbors of x

– This step can be checked easily using the index on fans and centers

Result: for (3,3)-cores, 5M pages remained Final step: – Since the graph is much smaller, we can afford

to “enumerate” the remaining cores

Step 3: using in-degrees of pages Delete pages highly references, e.g.,

yahoo, altavista Reason: they are referenced for many

reasons, not likely forming an emerging community

Formally: remove all pages with more than k inlinks (k = 50,for instance)

Results:– 60M pages pointing to 20M pages– 2M potential fans

Weakness of CT

The bipartite graph cannot suit all kinds of communities

The density of the community is hard to adjust

Experiment on CT

200 millions web pages

IBM PC with an Intel 300MHz Pentium II processor, with 512M of memory, running Linux

i from 3 to 10 and j from 3 to 20

200k potential communities were discovered29% of them cannot be found in Yahoo!.

Summary

Conclusion: The methods to discover communities from the web depend on how we define the communities through the link structure

Future works: How to relate the contents to link structure

Web communities based on dense bipartite graph patterns (WISE’01)

By Krishna Reddy and Masaru Kitsuregawa

Aim/Motivation

To find all the communities within a large collection of web pages.

Proposed solution:

•Analyze linkage patterns

•Find DBG in the given collection of webpages

DefinitionsBipartite graphA BG is a graph which can be partitioned into two non-empty sets T and I. Every directed edge of BG joins a node in T to a node in I

Dense Bipartite graphA DBG is a BG where each node of T establishes an edge with at least alpha nodes of I and each node of I has atleast beta nodes as parents to it CommunityThe set T contains the members of the community if there exist a DBG(T,I,alpha,beta) where alpha>= alpha_t and beta>=beta_t Where alpha_t and beta_t > 0.

DBG(T,I,p,q)

a

b

c

d

s

t

u

p q

Definitions

Cocite: Association among pages based on the existence of common children (URL’s).

Relax Cocite: we allow u,v,w to group if cocite(u,v) and cocite(v,w) are true.

p

q

a

b

c

d

e

f

i)

p

b

q

r

a

c

d

e

fg

Algorithm

1.For a given URL find T(set of URL’s). Relax-cocite factor is 1.

a)While num_iterations<=n At a fixed relax-cocite factor value,find all w’s

such that relax-cocite(w,y) =true T= w U T2. Community extraction

Input contains Page_set,outputDBG(T,I,alpha,beta)

Edge file has <p,q> where p is the parent of q.

Algorithm(contd…)

For each P belongs to T,insert the edge<p,q> in edge_file if q belongs child(q).

Sort edge file based on source.Prepare T1 with<source,freq>.Remove <p,q> from edge_file if freq<alpha.

Sort the edge_file based on destination.Prepare I1 with<q,freq>.Remove<p,q> from edgefile if freq<beta.

The result is a DBG(T,I,alpha,beta).

Advantages/Disadvantages

Extracts all DBG’s in a pageset. Community extracted is significantly large.

DISADV:

Need a URL to start with. Community members need links to be a

part of the community

Efficient Identification of Web Communities

Gary William Flake, Steve Lawrence & C. Lee Giles

Presentation Structure

Introduction or why they did it!

Motivation Background

Theory or how they did it!

Definition Algorithm

Experimentation or how did they do!

Results Conclusions

Motivation

Exploding Web ~ 1,000,000,000 documents Search Engine Limitations

Crawling the web 16% Maximum Coverage!

Updating the web Precision vs Recall

Web Communities Balanced Min Cut Identification is NP-hard

Background

Bibliometrics, Citation analysis, Social Networks

Classical Clustering eg. CORA

HITS hubs and authorities

s-t Max Flow & Min Cut

Floyd & Fulkerson’s Max Flow = Min Cut Theorem

Incremental Shortest Augmentation algorithm in poly-time

•Capacity weights

•Source & Sink

•Water In, Water Out!

G(V,E)

The Idea

The Ideal Community C V

Theorem1: A community C can be identified by calculating the s-t minimum cut using appropriately chosen source and sink nodes.

Proof by Contradiction

The Algorithm

1. Choose Source(s) and Sink(s)2. Generate G(V,E) using crawler3. Find s-t Min Cut

•Virtual Sources & Sinks•Choosing the Source•Choosing the Sink

Source layers Sink layers

Expectation Maximization

Implementation Issues Small size G(V,E) = low recall Dependent on choice of source set

Recurse over Algorithm Community obtained in one iteration used as

input to next iteration

Termination not guaranteed

Experimental Results

Testing neighborhoods … Support Vector Machine (SVM) The Internet Archive Ronald Rivest

Criterion Precision & Recall Seed set size Running time

SVM Community

Characterization Recent: Not listed in any portal Relatively small research community

Seed Set svm.first.gmd.de, svm.research.bell-labs.com,

www.clrc.rhbnc.ac.uk/research/SVM, www.support-vector.net

Performance 4 iterations of EM 11,000 URLs in the graph, 252 member web pages

Internet Archive Community

CharacterizationLarge, internal communities

Seed Set : 11 URLs Performance

2 iterations of EM 7,000 URLs, 289 web pages

Ronald Rivest Community

Characterization Community around an individual

Seed set http://theory.lcs.mit.edu/~rivest

Performance 4 iterations of EM 38,000 URLs, 150 pages Cormen’s pages as 1st and 3rd result

Summary

Actual running time 1 sec on a 500 MHz Intel machine

Max Flow Framework EM Approach Relevancy test

Applications

Focused crawlers Increased Precision & Coverage

Automated population of portal categories Recall Addressed

Improved filtering Keyword Spamming Topical Pruning – eg. Pornography

Future Work

Generalize the notion of Community Parameterize with coupling factor

Low value, weakly connected communities High value, highly connected communities

Ideal community Co-learning and Co-boosting

References

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Page Ranking

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Jon. Kleinberg, Journal of the ACM o The Anatomy of a Large-Scale Hypertextual

Web Search Engine (1998) Sergey Brin, Lawrence Page, Computer Networks and ISDN Systems.

o Web Search Via Hub Synthesis (1998) Dimitris Achlioptas,

Amos Fiat, Anna R. Karlin, Frank McSherry. o What is this Page Known for? Computing Web Page Reputatio

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Finding Authorities and Hubs From Link Structures on the World Wide Web, Allan Borodin, Gareth O. Roberts, Jeffrey S. Rosenthal, Panayiotis Tsaparas, 2002.

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Inferring Web Communities from Link Topology (1998) David Gibson, Jon Kleinberg, Prabhakar Raghavan, UK Conference on Hypertext.

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(1999) Jeffrey Dean, Monika R. Henzinger, WWW8 / Computer Networks.

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(2002) Yiming Yang, Sean Slattery, Rayid Ghani, Journal of Intelligent Information Systems.

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Documents

Web Mining : A Bird’s Eye View Sanjay Kumar Madria Department of Computer Science University of Missouri-Rolla, MO 65401 [email protected]