1. Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology Jeff Naruchitparames, Mehmet Gunes, Sushil J. Louis University of Nevada, Reno Evolutionary Computing Systems Lab (ECSL (excel)) http://ecsl.cse.unr.edu (jnaru@cse.unr.edu)

2. Outline Social Networks – Recommend facebook friends Approach Method Results Future Work

3. What is the problem? Recommend friends on facebook Customized to each user Use – Friends of friends – Degree centrality – Pareto Optimal GA GA identifies useful “social” features – Feature selection How do we figure out if we are making progress?

4. Prior Work Facebook seems to use a friend-of-friends approach. Analyze friend graphs to find cliques or communities (Kuan) Filter: GA used to optimize 3 parameters derived from structure of social network. Then filter based on these parameters (Last CEC, Silva) …more We also use a filtering approach based on features identified by a pareto-GA

5. Jeff’s Friends

6. Approach – Successive filtering Consider friends of friends (fof) Add users who have high degree centrality – Degree centrality = deg(v)/n-1 – N is number of vertices Personalize recommendations based on N social features Which M features from these N? – N == 10 in this paper – GA chooses M

7. Ten Features (1/2) 1.Number of Shared Friends 2.Number of friends in town 3.Age Range 4.General Interests 1.Number of shared likes, music 5.Common photos 1.Number of shared photo tags

8. Ten Features (2/2) Number of shared events Number of shared groups Number of liked movies Education – Same school with two year overlap Number of same: Religion and Politics

9. Caveats Preliminary work 10 features  10 bits  1024 points in search space. That’s easy for exhaustive search! But we want to – Test approach on a small problem first – Then expand to N >> 10 features

10. Methodology Representation Genetic Algorithm – Selects features to use for filtering – Pareto optimality principles to compare feature sets. Pareto front tells you which feature sets work well Best combination of features for each central person through Pareto optimality Feature 1  Present, 0  Absent

11. Pareto Genetic Algorithm Chromosome fitness is inverse pareto rank times number of friends Elitist GA, tournament selection Single point crossover (0.92) High mutation probability (0.89) Populations size = 20 Number of generations = 30 Results averaged over 3 runs on 100 users

12. Performance comparison method 100 users Remove 10 friends See if system recommends those 10 Track number of friends correctly recommended

13. Results GA filtering alone FOF + Dcentricity

14. Results

15. Conclusions and Future Work Pareto GA seems to help Pareto friendships seem promising as a representation Performance metric Lots of work left to do – Experiment with GA – Do we really need Pareto GA? – More features – Combinations with other approaches

16. While you ask Questions? http://ecsl.cse.unr.edu CI in RTS games: Research Assistantships