Group proximity measure for recommending groups in online social networks

1. Group proximity measurefor recommending groups in online social networks Presented by Sai Moturu BarnaSaha and LiseGetoor University of Maryland SNA-KDD Workshop ‘08 Oct 17

2. 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

3. 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.

4. 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’

5. 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

6. Obtain Concise Graph

7. Predicting Future Growth • Link Cardinality Estimation • Group Proximity Measure • Number of links in between • Product of the size of the two groups • Classification

8. Group Recommendation Models

9. Results

10. Results

11. 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

12. Thank You