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TU Berlin

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Analysis of Neural Data

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Towards community detection in k-partite k-uniform hypergraphs
Citation key Neubauer2009a
Author Neubauer, N. and Obermayer, K.
Title of Book Workshop on Analyzing Networks and Learning with Graphs, NIPS
Year 2009
Abstract Recently, numerous applications have emerged that create data most naturally interpreted as k-partite k-uniform hypergraphs. We identify benefits and challenges of generalizing community detection algorithms to these structures. We propose an algorithm which handles some of these challenges and a hypergraph generalization of the “caveman” model for the generation of synthetic evaluation datasets. The proposed algorithm outperforms a standard community detection algorithm working on a reduced, graph-like version of the original data, in particular when different domains possess differing community structures.
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