Papers › Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters

Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters

13 Aug 2017KDD 2017 8archive 2025-07-28

Alessandro Epasto, Silvio Lattanzi, Renato Paes Leme

We propose a new framework called Ego-Splitting for detecting clusters in complex networks which leverage the local structures known as ego-nets (i.e. the subgraph induced by the neighborhood of each node) to de-couple overlapping clusters. Ego-Splitting is a highly scalable and flexible framework, with provable theoretical guarantees, that reduces the complex overlapping clustering problem to a simpler and more amenable non-overlapping (partitioning) problem. We can solve community detection in graphs with tens of billions of edges and outperform previous solutions based on ego-nets analysis. More precisely, our framework works in two steps: a local ego-net analysis phase, and a global graph partitioning phase . In the local step, we first partition the nodes’ ego-nets using a partitioning algorithm. We then use the computed clusters to split each node into its persona nodes that represent the instantiations of the node in its communities. Then, in the global step, we partition the newly created graph to obtain an overlapping clustering of the original graph.

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ClusteringCommunity Detectiongraph partitioning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Community Detection Amazon Ego-Splitting F1-score 0.0374 #2 of 3 Archive leaderboard report
Community Detection Amazon Ego-Splitting NMI 0.089 #2 of 3 Archive leaderboard report

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