Social Market: Combining Explicit and Implicit

Social Networks

Nithyakumaran Gnanasekar

Overview

• Motivation• Motivating Example• Problem• Social Market• TAPS• Experimental Results• Reference

Motivation

• Social network can be split into two categories.o Explicit Network.o Implicit Network.

• Explicit Networko Reinforces Existing Real World Connections

• Implicit Networko Forms Dynamic communities based on mutual interest,

common activities, places etc.

• The idea is to bring about a convergence between implicit and explicit networks.

Motivating Example

Problem:

Combining explicit and implicit social networks has huge cost.

Enormous amount of information necessary to be managed.

One Possible solution is to use internet-based Gossip overlays.

Social Market

System model:

Consider system of interconnected users exchanging information.

Each user has a profile associated

Profile is vector of strings

Each string is referred to as "Keyword"

Every keyword has a counter and a weight associated.

Social Market

Weigth measure of relavance between a given keyword to other keywords in the profile.

where U is universe of all profiles.

And u is denotes user or user profile.

Cosine Similarity :

Social Market

Items:

• User interact with social market by creating items.

• Every item has a profile and is stored in a similar fashion as User profiles.

Once a item is created, goal of social market is to lead this item to meet other user who

• Are interested in the item

• Can be trusted and can trust the creator of the item

• Can be reached through a trusted path on the social network

Social Market

SM uses a feature called trust to build this trust path.

The trust between users are provided by the users themselves.

For instance, User A can assign 0 trust on user B.

0 trust doesn’t mean, User A distrusts B, simply means that A does not know B enough.

Social Market

Trust Aware Peer Sampling

A novel protocol that operates by directly incorporating trust relationships.

Extracted from an explicit social network into the gossip-based overlay.

• Goal:o Create TAPS view with ever changing set of reference

to other nodeso Periodically, nodes contact to exchange information of

their views

Trust Aware Peer Sampling

• In standard peer sampling contains:o Contact information of other nodeso Timestamp indicating last update.

• TAPS contain information:o User profileo Inferred trusts value.

Trust propogation

• Each edge in the trusted path associates uncertainty about the trustworthiness.

• To model inferred trust.o Trust path as product of trust values of its edges,

weighted by trust transitivity co efficient .o Given path u1, u2, … un with trust values t1,2 , t2,3, .. tn-

1,n

o Lower values causes trust to decay faster with path length.

View Exchanges

o Views are initialized with agreed upon trust value during explicit friendship relationships.

o Initialize TAPS view by inserting one entry of each explicit neighbors.

o These views are exchanged with other nodes.o View are exchanged between friends, friends of

friends of friends.

View Exchanges

o As gossip process evolves nodes collaborate computing inferred trust.

o Let trust of Nodes A and X be tA,X and trust of A and B be tA,B , to compute tB,X

tBX = τtABtAX.

.

View Exchanges

o A node might receive views from multiple nodes. A node always selects the largest trust value for

any node.o To enchance trust inference, nodes initiate gossip

exchanges with nodes in TAPS view and explicit neighbours.

o The trust path values are kept up to date and maximum trust path is chosen to provide shortest path.

Evaluation

o Dataset of 300 users where taken from facebook and Digg. Binary Trace Multivalued Trace

Impact of trust density

Evaluation

BinaryMulti Valued

Binary Multi Valued

Impact of Trust Transitivity.

Impact of Trust Weight.

ReferenceFrey, Davide, Arnaud Jégou, and Anne-Marie Kermarrec. "Social market: combining explicit and

impBertier, Marin et al. "The gossple anonymous social network." Middleware 2010 (2010): 191-211.licit social networks." Stabilization, Safety, and Security of Distributed Systems (2011): 193-207.

Bertier, Marin et al. "The gossple anonymous social network." Middleware 2010 (2010): 191-211.

Questions