Thursday, 13:15 - 13:40 h, Room: H 3002

 

Michael Ovelgönne
Ensemble learning for combinatorial optimization: Modularity maximization and beyond

Coauthor: Andreas Geyer-Schulz

 

Abstract:
Modularity maximization is the NP-hard problem of identifying a graph partition with a maximal value of the quality measure modularity. Modularity maximization is a well-studied problem in the area of community detection in networks and attracted much attention in computer science as well as physics. A vast number of algorithms have been proposed for this problem. The core groups graph clustering (CGGC) scheme is an ensemble learning clustering method with very high optimization quality. This method combines the local solutions of several base algorithms to form a good start solution (core groups) for the a final algorithm. Especially iteratively finding good restart points showed to result in very good optimization quality. We will draw an analogy between the discrete problem of modularity maximization with nonlinear optimization in finite dimensions. We will show that core groups are the discrete counter-parts of saddle-points and that they constitute good restart points for greedy algorithms. While we developed the CGGC scheme for graph clustering, we believe this optimization scheme can be applied to many other combinatorial optimization problems as well.

 

Talk 1 of the invited session Thu.2.H 3002
"Network clustering" [...]
Cluster 23
"Telecommunications & networks" [...]

 

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