GROUP PROXIMITY MEASURE FOR RECOMMENDING GROUPS IN ONLINE SOCIAL NETWORKSBarna Saha and Lise GetoorUniversity of MarylandSNA-KDD Workshop ‘08

Presented by Sai Moturu

Oct 17

OVERVIEW Setting: Communities in Online Social Networks

Goal: Recommending groups/communities to users

Problem: Defining proximity between communities

Approach: Group Proximity Measure

Experiments: Flickr, Live Journal, You Tube

ESCAPE PROBABILITY Ei,j – Escape probability from i to j – probability that a

random walk from node i will visit node j before visiting i Vk(i,j) – Probability that a random walk from node k will

visit node j before visiting node i

Computed using the Fast algorithm by Tong et al.

APPROACH OUTLINE Let Gi and Gj be two groups Ci/Cj represents the core and Oi/Oj represents the

outliers Find CORE

Find Ci & Cj Obtain Concise Graph

Shrink Ci & Cj into two vertices Vi & Vj Remove self loop and replace parallel edges with a single

edge and representative weight Call the concise graph G’

Compute Escape Probability in G’

FINDING CORE Degree Centrality

For a node, its degree in the group is the number of members of the group it is linked to

Pick all members with a degree above a certain threshold Subgraph

Pick the subgraph within a group that has maximum ratio of edges/vertices

OBTAIN CONCISE GRAPH

PREDICTING FUTURE GROWTH Link Cardinality Estimation

Group Proximity Measure Number of links in between Product of the size of the two groups

Classification

GROUP RECOMMENDATION MODELS

RESULTS

RESULTS

CONTRIBUTIONS New link-base proximity measure for groups in online

social networks

Using proximity measure and structural properties to predict number of new links that will develop between two groups

New recommendation system based on group proximity and history of user’s group membership

THANK YOU