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Structural Analysis in Large NetworksObservations and ApplicationsMary McGlohon
CommitteeChristos Faloutsos, co-chairAlan Montgomery, co-chairGeoffrey GordonDavid Jensen, University of Massachusetts, Amherst
Motivation•Network (a.k.a. graph, relational, social
network) data has become ubiquitous. We want to know:▫How do networks form and structure
themselves?▫How does information propagate through
networks?▫How do sub-communities form?
2Facebook
Computer networksIMDB actor-movie
1 1 2 2
3 3
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
3
“Outline” for thesis
1 1
2 2
3 3
Motivation: Topology
•How do these network strucures form?▫Example: identify topological properties
common to many different types of graphs (citations, friendships, etc.)
▫Developing models of these properties allows for forecasting.
4
vs
1 1
Graph topology
Motivation: Cascades
•Once the networks form, how does information propagate through the graph?▫Example: Extract, analyze, and model
cascades.
5
Cascade
2 2
Motivation: Community
•How do we compare communities, or sub-networks?▫Example: For a set of online groups
(Usenet), which ones continue to thrive over time?
6
2004
2008?
3 3
Thesis statement
•We propose to ▫investigate how interactions in graphs
occur, how these interactions lead to diffusion and community behavior, and
▫to model these behaviors and apply these findings to real-world problems.
7
1 1 2 2 3 3
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
8
investigate how interactions in graphs occur,
how these interactions lead to diffusion
and community behavior, and
to model these behaviors and apply these findings to real-world problems.
We propose to…
Impact•Understanding the relations found in
networks has many applications, such as:
• Fraud/anomaly detection▫Given typical behavior and information about
nodes/edges, how “suspicious” is a node or group of nodes?
•Ad personalization/recommendation systems▫Given some information about an individual and
their friends, which ads to display?•Resource allocation▫Given typical patterns of network growth, how
can we allocate resources (hardware, advertising budget, etc.)?
9
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
KDD08ICDM08
ICWSM07
ICWSM09-2*ICWSM09-3*
KDD09*
ICWSM09-1*
10
Completed Work
*- to appear
SDM07
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
11
Proposed Work
P1a: How do cascades compare across network structures?
P2: Can we predict success/failure of groups?
P1b: Can we use cascades to model product adoption?
The rest of the talk
•Motivation and thesis statement•Completed work•Proposed work•Conclusions and impact•Audience participation!
12
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
What patterns are common to networks?
13
Completed Work
Topological Observations
•Diameter over time
14
•Connected components
•Edge weights
(Kevin Bacon)
Topological Observations: Data
•Analyze unipartite and bipartite networks•Networks are evolving over time•Networks may be weighted
15
n1
n2
n3
n4
n5
n6
n7
310
1.2
8.3
2
6
1
n1
n2
n3
n4
m1
m2
m3
-Repeated edges-Edge weights
10
1.2
8.3
2
6
1
3UnipartiteCitations,Blogs,Router traffic
BipartiteIMDB Actor-Movie,Campaign contributions…
Topological Observations: Gelling Point•When does a graph begin displaying
expected patterns, such as the giant connected component? How can we tell when this happens?
16
Topological Observations: Gelling Point•Observation: Most real graphs display a
gelling point, where the graph begins to come together and the giant connected component forms. After that point, they exhibit typical behavior.
17Time
Diameter
IMDB
t=1914
Topological Observations: NLCCs• In graphs a giant
connected component emerges.
•We look at sizes of the next-largest connected components (NLCCs)
•After gelling point, do they continue to grow? Do they shrink?
18
Topological Observations: NLCCs•Observation: After the gelling point, the
giant connected component takes off, but next-largest connected components remain constant or oscillate.
19
Time
IMDB
Size of next-
largest connected componen
ts
t=1914) ia2nd connected component
3rd connected component
Topological Observations: Weights•How are edges in a graph repeated, or
otherwise weighted?•As the number of edges increases, does
the total edge weight grow linearly?
20
Topological Observations: Weights•Observation: Weight additions follow a
power law with respect to the number of edges:
W(t) ∝ E(t)w
▫W(t): total weight of graph at t▫E(t): total edges of graph at t▫w is PL exponent (w>1)
•Many other weighted laws: see [KDD08, ICDM08]
21log(Edges)
log(Weights)
Orgs-Candidates
slope=1.3
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
What patterns are common to networks?
22
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
23
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Can we develop generative models?
24
Completed Work
Topological Models: “Butterfly”
•Goals are to generate:▫Constant/oscillating NLCC’s▫Densification power law [Leskovec+05]
▫Shrinking diameter (after “gelling point”)▫Power-law degree distribution▫Emergent, local, intuitive behavior
25
Topological Models: “Butterfly”
•Main idea: Uses 3 parameters▫“Curiosity”: how much to explore local
network (~U(0,1), creates power-law degree distribution)
▫ “Flyout”: how many local networks to explore (global, joins components)
▫“Friendliness”: how often to connect (global, allows new components)
•Details: see [KDD08]
26
Topological Models: “Butterfly”
27Log(degree)
Log(count)slope=-2
Power-law degree distributionNodes
Diam-eter
Shrinking diameter
log(nodes)
log(edges) slope=1.1
7
Densification
Nodes
NLCCsize
Oscillating NLCCs
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Can we develop generative models?
28
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
ButterflyRTMOddball
29
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
What are patterns of cascades in networks?
ButterflyRTM Oddball
30
Completed Work
Cascade Observations: Data
•Gathered from August-September 2005*•Used set of 44,362 blogs, traced cascades•2.4 million posts•245,404 blog-to-blog links
31Time [1 day]
Nu
mb
er
of
post
s
Jul 4
Aug 1Sep 29
Cascade Observations: Prelims
32
Blogosphere
B1 B2
B4B3
Cascades
d
e
b c
e
a
a
b c
de
“Star” “Chain”
•How quickly does a link to a post occur?•What size do cascades typically reach?•What are typical shapes– how often are
“stars” and “chains” occurring?
33
Temporal Observations
• How quickly does a link to a post occur?• Does popularity decay at a constant rate?
• With an exponential (“half life”)?
Linear-linear scale Log-linear scale Log-log scale
Cascade Observations: Link Popularity•Observation: The probability that a post
written at time tp acquires a link at time tp + Δ is:
p(tp+Δ) ∝ Δ-1.5
• Similar to [Vazquez+06]
34log(days after post)
log(
# in-l
inks
)slope=-1.5
(Linear-linear scale)
Cascade Observations: Cascade Size•Q: What size distribution do cascades follow?
Are large cascades frequent?
•Observation: The probability of observing a cascade of n blog posts follows a Zipf distribution:
p(n) ∝ n-2
35log(Cascade size) (# of nodes)
log(C
oun
t)
slope=-2d
e
b c
e
a
log(Size) of chain (# nodes)
log(C
oun
t)
a=-8.5
log(Size) of star (# nodes)
log(C
oun
t) a=-3.1
Cascade Observations: Cascade Size•Q: What is the distribution of particular cascade
shapes?•Observation: Stars and chains in blog cascades
also follow a power law, with different exponents
(star -3.1, chain -8.5).
36
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
What are patterns of cascades in networks?
ButterflyRTM Oddball
37
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Cascades lawsCascades as
features
ButterflyRTMOddball
38
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Cascades lawsCascades as
features
Can we develop predictive models for cascades?
ButterflyRTMOddball
39
Completed Work
Cascade Models: CGM
•Cascade Generation Model•Overview: Produce realistic cascades
through an emergent “viral” model•Details: See [SDM07]
40
Cascade Models: CGM
41
Most frequent cascades
model
data
log(Cascade size) (# nodes)
log(C
ou
nt
)
log(C
oun
t)
log(Star size)
log(C
oun
t)
log(Chain size)
DataModel
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Cascades lawsCascades as
features
Can we develop predictive models for cascades?
ButterflyRTMOddball
42
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Cascades lawsCascades as
features
Cascade generation model
ZC model
Butterfly RTMOddball
43
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Cascades laws Cascades as
features
How can we compare communities?
Cascade generation model
ZC model
Butterfly RTM Oddball
44
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Cascades laws Cascades as
features
Political Usenet study
Cascade generation model
ZC model
Butterfly RTM Oddball
45
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Cascades laws Cascades as
features
Political Usenet study
Can we detect anomalies?
Cascade generation model
ZC model
Butterfly RTM Oddball
46
Completed Work
Community Tools: SNARE
•Problem: Given a network and some domain knowledge about suspicious nodes (flags), determine which nodes are most risky.
•Data: Accounting transaction data. Nodes are accounts, edges are transactions between accounts.
47
Accounts Payable
Accounts Receivable
Revenue Accts
Community Tools: SNARE
•Example: “Channel stuffing”▫Some accounts overstated▫But other accounts also involved.▫Since many accounts are slightly affected, it
is easy to cover up activity.
48
Accounts Payable
Accounts Receivable
Revenue Accts
Very risky
Not risky
Community Tools: SNARE
49
•Social Network Analytic Risk Evaluation▫Use domain knowledge to flag certain nodes.▫Assume homophily between nodes (“guilt by
association”)▫Then, using initial risk as initial node potentials,
use belief propagation (message passing between nodes) to determine end risk scores.
Community Tools: SNARE
50
•Belief Propagation▫Flags are node potentials, or “intial risk scores”▫All nodes send messages back and forth with
beliefs▫Upon convergence, end result will reflect
“riskiest” nodes.
After
Revenue Accts
Before
Accounts Payable
Accounts Receivable
Community Tools: SNARE
51
•Produces improvement over simply using flags▫Up to 6.5 lift▫Improvement especially for low false positive
rate
False positive rate
True positive
rate
Results for accounts data (ROC Curve)Ideal
SNARE
Baseline (flags only)
Community Tools: SNARE
52
•Accurate- Produces large improvement over simply using flags
•Flexible- Can be applied to other domains•Scalable- One iteration BP runs in linear time
(# edges)•Robust- Works on large range of parameters
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Cascades laws Cascades as
features
Political Usenet study
Can we detect anomalies?
Cascade generation model
ZC model
Butterfly RTM Oddball
53
Completed Work
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
Gelling point, CC’s
Weighted laws
Cascades laws Cascades as
features
Political Usenet study
SNARE
Cascade generation model
ZC model
Butterfly RTM Oddball
54
Completed Work
The rest of the talk
•Motivation and thesis statement•Completed work•Proposed work•Conclusions and impact•Audience participation!
55
Proposed Work
•2 main problems:▫P1: Cascades and product adoption
How do cascades vary according to network structure?
Can we use cascades to model product adoption?
▫P2: Predicting success/failure of online groups
56
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
57
P1a: How do cascades compare across network structures?
P2: Can we predict success/failure of groups?
P1b: Can we use cascades to model product adoption?
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
58
P1a: How do cascades compare across network structures?
P2: Can we predict success/failure of groups?
P1b: Can we use cascades to model product adoption?
• In different networks, how does starting point of an epidemic affect the epidemic size?
•What modifications on current model changes the cascades (weights, self-infection)?
•Can we reverse-engineer network properties based on observed cascades?
P1a: Cascades & Network Structure
59
Many hubs?Large diameter?
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
60
P1a: How do cascades compare across network structures?
P2: Can we predict success/failure of groups?
P1b: Can we use cascades to model product adoption?
P1b: Cascades & Product Adoption
•Examine adoption of Caller Ringback Tones (CRBT)▫User buys ringtone▫Friend calls user, hears CRBT
•Phone call data▫Nodes: User ID, DOB, salutation (Mr/Ms),
date of joining, data plan▫Call Edges: src/dest ID, call time, duration▫SMS Edges: src/dest ID, time▫CRBT purchases: purchase date, song
name, cost
61
P1b: Cascades & Product Adoption•Can we fit the Bass Model for different
CRBT’s?
62
# adopters today
# adopters yesterday
# potential adopters
“mass marketing”
“word of mouth”
P1b: Cascades & Product Adoption
•Are some CRBT’s more “viral” than others? Does the footprint follow a skewed distribution?
•How long after purchase is a CRBT infective?
63Number of downloads (per song)
Survival Functio
nP(X>x)
P1b: Cascades & Product Adoption
•How does the weight of a link, homophily, or other factors affect the likelihood of transmission?
•Can we explicitly test whether a purchase is a result of basic similarity of neighbors or a result of “viral” propagation?
•How can we build and verify a model for this propagation?
64
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
65
P1a: How do cascades compare across network structures?
P2: Can we predict success/failure of groups?
P1b: Can we use cascades to model product adoption?
P2: Success & Failure of Online Groups•Use data over 4 years from nearly 200
newsgroups. (Political Usenet)•Many discussion groups stop posting by
the third year.
•Why?
66
P2: Success & Failure of Online Groups•P2 Questions:▫If structural network characteristics can be
traced to success or failure, which features are most predictive?
▫Can we test causality in the predictive characteristics?
67
Timeline
68
May 09
Jun ‘09
Sep ‘09
Nov ‘09
Mar ‘10
Aug ‘10
Jul ‘10
P1 preliminaries
Internship at Google
P1a: Cascades and network structureP1b: Cascades and product adoption
P2: Success/failure of online groupsComplete document
Defend
Related work▫ Topology:
Heavy-tailed degree distributions [Faloutsos+99] [Albert+02] [Kleinberg+99]
Shrinking diameter, densification [Leskovec+05] Random graphs model [Erdos+60] “Forest Fire” model [Leskovec+05] “Winners do not take all” model [Pennock+02]
▫ Cascades Recommendations: [Leskovec+06] Diffusion in blogs: [Adar+03] [Gruhl+04] [Kempe+03]
[Kumar+03] Marketing: Product adoption [Bass69], Word-of-mouth
[Godes+04] Virus propagation: Populations [Hethcote], Networks
[Boguna, Pastor-Satorras] [Charkabarti]▫ Communities and other applications
Securities fraud detection [Neville+05] [Fast+07] Author identification [Hill+04] Online group behavior [Backstrom+08]
69
Conclusions: Completed
•Demonstrated several properties common to networks in a wide range of domains.▫Oscillating sizes of next-largest connected
components▫Power laws for weighted graphs▫Butterfly model: generates properties
70
Conclusions: Completed
•Studied and modeled cascades in blogs▫Several power laws for cascade shapes and
size▫Cascade Generation Model
•Devised SNARE for anomaly detection for accounting data (lift factor up to 6.5)
71
Conclusions: Proposed
•P1a: Continue cascade studies across network structures
•P1b: Use cascades to model purchases in phone-call graph
•P2: Build predictive models for success and failure in online groups
72
ReferencesTopology[KDD08] M. McGlohon, L. Akoglu, and C. Faloutsos. Weighted
Graphs and Disconnected Components: Patterns and a Generator. SIG-KDD. Las Vegas, Nev., August 2008.
[ICDM08] L. Akoglu. M. McGlohon, and C. Faloutsos. RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs. ICDM. Pisa, Italy, Dec. 2008.
Cascades[SDM07] J. Leskovec, J, M. McGlohon, C. Faloutsos, N. Glance, and
M. Hurst. Patterns of Cascading Behavior in Large Blog Graphs. SDM. Minneapolis, Minn., April 2007.
[ICWSM07] M. McGlohon, J. Leskovec, C. Faloutsos, N. Glance, and M. Hurst. Finding patterns in blog shapes and blog evolution. ICWSM. Boulder, Colo., March 2007.
[ICWSM09-1] M. Goetz, J. Leskovec, M. McGlohon, and C. Faloutsos. Modeling Blog Dynamics. ICWSM. San Jose, Cali. May 2009.
73
References
Community
[KDD09] M. McGlohon, S. Bay, M. Anderle, D. Steier, and C. Faloutsos. SNARE: A Link Analytic System for Evaluating Fraud Risk. ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG-KDD). Paris, France. June 2009.
[ICWSM09-2] M. McGlohon and M. Hurst. Community Structure and Information Flow in Usenet: Improving analysis with a thread ownership model. International Conference on Weblogs and Social Media (ICWSM). San Jose, CA. May 2009.
[ICWSM09-3] M. McGlohon and M. Hurst. Considering the Sources: Comparing linking patterns in Usenet and blogs. International Conference on Weblogs and Social Media (ICWSM09). San Jose, CA. May 2009.
74
75
▫Support: ▫ PricewaterhouseCoopers▫ Microsoft Live Labs▫ NSF Graduate Research Fellowship▫ Yahoo! Key Technical Challenges Grant, Pennsylvania
Infrastrucutre Technology Alliance (PITA)▫ Hewlett-Packard▫ NSF Grants No. IIS- 0705359, IIS-0534205, and CNS-0721736,
0209107, SENSOR-0329549, EF-0331657, IIS-0326322▫ U.S. Department of Energy Lawrence Livermore National
Laboratory contract No.W-7405-ENG-48.
Audience participation!
76
77
Talk expansion pack
78
P1b: Other Cascade Data•Post data from corporate blogs▫Demographic data on bloggers (employee
ID, location, job description)▫Read data (timestamped)▫Write data (timestamped)
•CRBT adoption in general▫Perhaps people do not adopt particular
songs, but the CRBT mechanism•More public blog data (spinn3r)▫Also use edge information from
blogrolls/comments
79
P2: Potential features to examine•Posting behavior▫Which users are posting, how often are they
posting, and how skewed is the distribution?•Linking behavior▫How long are cascades (threads), in terms
of post and time?•Content▫Topics, keywords, sentence length, other
textual features, sentiment analysis
80
Unipartite Networks• Postnet: Posts in blogs,
hyperlinks between• Blognet: Aggregated Postnet,
repeated edges• Patent: Patent citations• NIPS: Academic citations• Arxiv: Academic citations• NetTraffic: Packets, repeated
edges• Autonomous Systems (AS):
Packets, repeated edges
n1
n2
n3
n4
n5
n6
n7
81
4 million nodes 8 million edges
17 years
4 million nodes 8 million edges
17 years
Bipartite Networks
• IMDB: Actor-movie network• Netflix: User-movie ratings• DBLP: conference- repeated
edges▫Author-Keyword▫Keyword-Conference▫Author-Conference
• US Election Donations: $ weights, repeated edges▫Orgs-Candidates▫ Individuals-Orgs
n1
n2
n3
n4
m1
m2
m3
82
6 million nodes 10 million edges
22 years
6 million nodes 10 million edges
22 years
Topological Models: “Butterfly”
83
•New node joins, picks host and iteratively random walks around neighbors, with ~ U(0,1)▫Some nodes “friendlier” than others
•Nodes may have multiple hosts ( ).▫ Joins components
•Nodes link with probability ▫May choose host, but not link (start new
component)
new node
host
Topological Models: “Butterfly”
•Nodes may have multiple hosts ( ).▫ Joins components
84
•Node picks “host” and iteratively perform random walk around neighbors, with ~ U(0,1)▫Some nodes “friendlier” than others
•Nodes link with probability ▫May choose host, but not link (start new
component)
Topological Models: RTM
•Recursive Tensor Model•Goal: to introduce time and burstiness•Main idea: Begin with a core tensor
(multidimensional array), and use self-similarity to reproduce observed power laws.
85
Topological Models: RTM
•Self similarity arises from Kronecker product
•2D:
86
[Leskovec+06]
Topological Models: RTM•3D: Use Kronecker product on a core tensor
•Reproduced power laws as found in ICDM08
87
Adjacency matrix
Topological Models: RTM•3D: Use Kronecker product on a core tensor
•Reproduced power laws as found in ICDM08
88
3rd dim: time
Topological Applications: Oddball•Main ideas:▫Use local neighborhood of node▫Find common patterns▫Score how much a node deviates from
common patterns•Results▫Identified anomalous nodes such as Ken Lay
in Enron, particularly different blog posts
89
Cascade Models: CGM
90
B1 B2
B4B3
ii) Infect each in-linked neighbor with probability
p1,1
B1 B2
B4B3
iii) Add infected neighbors’ posts to cascade.
p1,1
p4,,1
i) Randomly pick blog to infect, add post to cascade.
p1,1
B1 B2
B4B3
B1 B2
B4B3
iv) Set node infected in (i) to uninfected.
p1,1
p4,1
Cascade Models: Zero-crossing
•Main ideas:▫Models blogs in both network growth and
network diffusion▫Choose to post based on random walk
(produces burstiness)▫Link based on recency an popularity
(reproduces “-1.5 law” and skewed degree)▫Improvement over CGM because network is
generated
91
Community Observations: Newsgroups•Observation: Threads introduced to a group
later in the thread tended to have more activity from that group.
•Observation: Discussions tended to flow from “main” groups (can.politics) into subgroups (ab.politics, bc.politics)
92
Community Observations: Newsgroups•189 newsgroups (‘polit’ in name), January
2004-June 2008•37 million posts• Includes many countries, provinces,
states, topical groups (alt.politics.guns)
93
Major issue: over half are cross-posted to multiple groups.Where is conversation truly occurring?
{alt.politics, us.politics}
{alt.politics, us.politics}
{alt.politics,
us.politics, pa.politics}{alt.politics
, us.politics,
pa.politics}
Community Observations: Newsgroups•Solution: Introduce “Thread ownership”,
by assigning threads according to where authors exclusively post.
94
Community Observations: Newsgroups•Observation: Discussions tended to flow from
“main” groups (can.politics) into subgroups (ab.politics, bc.politics)
95
TOPOLOGY
COMMUNITY
CASCADES
OBSERVATIONS APPLICATIONS/TOOLS
What patterns are common to networks?
What are patterns of cascades in networks?
How can we compare communities?
Can we detect anomalies, and predict group behavior?
Can we develop predictive models for cascades?
Can we develop generative models and detect anomalies?
96
Completed Work