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

KDD 2017 Alessandro EpastoSilvio LattanziRenato 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... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Community Detection Amazon Ego-Splitting NMI 0.089 # 1
F1-score 0.0374 # 2

Methods used in the Paper


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