1. G ROUP PROXIMITY MEASURE FOR RECOMMENDING GROUPS IN ONLINE SOCIAL NETWORKS Barna Saha and Lise Getoor University of Maryland SNA-KDD Workshop ‘08 Presented by Sai Moturu Oct 17

2. O VERVIEW 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

3. E SCAPE P ROBABILITY E i,j – Escape probability from i to j – probability that a random walk from node i will visit node j before visiting i V k (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.

4. A PPROACH O UTLINE Let G i and G j be two groups C i /C j represents the core and O i /O j represents the outliers Find CORE Find C i & C j Obtain Concise Graph Shrink C i & C j into two vertices V i & V j Remove self loop and replace parallel edges with a single edge and representative weight Call the concise graph G’ Compute Escape Probability in G’

5. F INDING 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

6. O BTAIN C ONCISE G RAPH

7. P REDICTING F UTURE G ROWTH Link Cardinality Estimation Group Proximity Measure Number of links in between Product of the size of the two groups Classification

8. G ROUP R ECOMMENDATION M ODELS

9. R ESULTS

11. C ONTRIBUTIONS 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

12. T HANK Y OU