A Streaming Algorithm for Graph Clustering

Alexandre Hollocou 1, 2 Julien Maudet 3 Thomas Bonald 1, 4 Marc Lelarge 2
2 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique : UMR 8548, Inria de Paris
Abstract : We introduce a novel algorithm to perform graph clustering in the edge streaming setting. In this model, the graph is presented as a sequence of edges that can be processed strictly once. Our streaming algorithm has an extremely low memory footprint as it stores only three integers per node and does not keep any edge in memory. We provide a theoretical justification of the design of the algorithm based on the modularity function, which is a usual metric to evaluate the quality of a graph partition. We perform experiments on massive real-life graphs ranging from one million to more than one billion edges and we show that this new algorithm runs more than ten times faster than existing algorithms and leads to similar or better detection scores on the largest graphs.
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Communication dans un congrès
NIPS 2017 - Wokshop on Advances in Modeling and Learning Interactions from Complex Data, Dec 2017, Long Beach, United States. pp.1-12
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Dernière modification le : vendredi 31 août 2018 - 09:12:07

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Alexandre Hollocou, Julien Maudet, Thomas Bonald, Marc Lelarge. A Streaming Algorithm for Graph Clustering. NIPS 2017 - Wokshop on Advances in Modeling and Learning Interactions from Complex Data, Dec 2017, Long Beach, United States. pp.1-12. 〈hal-01639506v2〉

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