A Graph-Based Approach
to Link Prediction in Social
Networks Using a Pareto-
Optimal Genetic Algorithm
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Graph-Based Approach for Link Prediction with Genetic Algorithm, Slides of Computer Networks
A graph-based approach to link prediction in social networks using a pareto-optimal genetic algorithm. The author explores the limitations of traditional friend-of-friend filtering and introduces components such as betweenness centrality, community detection, and clique percolation method (cpm) for filtering. The document also covers the implementation of a 10-dimensional pareto-optimal genetic algorithm for feature subset selection. The goal is to find the best combination of features that can determine friendships in social networks.
Typology: Slides
2012/2013
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A Graph-Based Approach
to Link Prediction in Social
Networks Using a Pareto-
Optimal Genetic Algorithm
- biological
social
‣ Social networks = ‣ Dynamic, judgmental environment ‣ Affect friendships over time
heterogeneous very dynamic
‣ 1-2 hop distance only ‣ Friend-of-friend
‣ Multiple hops; > ‣ Structural; purely graph-based ‣ No explicit correlation between potential friends...
Filtering
“It’s more probable that you know a friend of your
friend than any other random person”
Mitchell M., Complex Systems: Network Thinking , 2006.
Indexes
What’s missing? ‣ Heterogeneity ‣ Human behavior and preferences ‣ Multiple hops
My approach ‣ Components (for filtering) ‣ Betweenness centrality ‣ Community detection ‣ Clique Percolation Method (CPM) ‣ Friends of friends ‣ 10-dimensional Pareto-optimal genetic algorithm