friend recommendations in social networks using genetic algorithms and network topology jeff...
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![Page 1: Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology Jeff Naruchitparames, Mehmet Gunes, Sushil J. Louis University](https://reader036.vdocuments.us/reader036/viewer/2022082214/56649d2f5503460f94a07202/html5/thumbnails/1.jpg)
Friend Recommendations in Social Networks using Genetic Algorithms
and Network Topology
Jeff Naruchitparames, Mehmet Gunes, Sushil J. Louis
University of Nevada, RenoEvolutionary Computing Systems Lab (ECSL (excel))
http://ecsl.cse.unr.edu ([email protected])
![Page 2: Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology Jeff Naruchitparames, Mehmet Gunes, Sushil J. Louis University](https://reader036.vdocuments.us/reader036/viewer/2022082214/56649d2f5503460f94a07202/html5/thumbnails/2.jpg)
Outline
• Social Networks– Recommend facebook friends
• Approach• Method• Results• Future Work
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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?
![Page 4: Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology Jeff Naruchitparames, Mehmet Gunes, Sushil J. Louis University](https://reader036.vdocuments.us/reader036/viewer/2022082214/56649d2f5503460f94a07202/html5/thumbnails/4.jpg)
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
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Jeff’s Friends
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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
![Page 7: Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology Jeff Naruchitparames, Mehmet Gunes, Sushil J. Louis University](https://reader036.vdocuments.us/reader036/viewer/2022082214/56649d2f5503460f94a07202/html5/thumbnails/7.jpg)
Ten Features (1/2)
1. Number of Shared Friends2. Number of friends in town3. Age Range4. General Interests
1. Number of shared likes, music
5. Common photos1. Number of shared photo tags
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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
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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
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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
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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
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Performance comparison method
• 100 users• Remove 10 friends• See if system recommends those 10• Track number of friends correctly
recommended
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Results
![Page 14: Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology Jeff Naruchitparames, Mehmet Gunes, Sushil J. Louis University](https://reader036.vdocuments.us/reader036/viewer/2022082214/56649d2f5503460f94a07202/html5/thumbnails/14.jpg)
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
![Page 15: Friend Recommendations in Social Networks using Genetic Algorithms and Network Topology Jeff Naruchitparames, Mehmet Gunes, Sushil J. Louis University](https://reader036.vdocuments.us/reader036/viewer/2022082214/56649d2f5503460f94a07202/html5/thumbnails/15.jpg)
While you ask Questions?
http://ecsl.cse.unr.eduCI in RTS games: Research Assistantships