Ensemble-Based Algorithms to Detect Disjoint and Overlapping Communities in Networks
Tanmoy Chakraborty, Noseong Park, V.S. Subrahmanian

TL;DR
This paper introduces two ensemble algorithms, EnDisCO and MeDOC, for detecting disjoint and overlapping communities in networks, demonstrating superior performance over existing methods through extensive experiments.
Contribution
It presents novel ensemble algorithms for community detection, including the first ensemble method for overlapping communities, outperforming state-of-the-art approaches.
Findings
Both algorithms outperform non-ensemble methods significantly.
EnDisCO effectively identifies disjoint communities.
MeDOC successfully detects overlapping communities.
Abstract
Given a set of community detection algorithms and a graph as inputs, we propose two ensemble methods and that (respectively) identify disjoint and overlapping communities in . transforms a graph into a latent feature space by leveraging multiple base solutions and discovers disjoint community structure. groups similar base communities into a meta-community and detects both disjoint and overlapping community structures. Experiments are conducted at different scales on both synthetically generated networks as well as on several real-world networks for which the underlying ground-truth community structure is available. Our extensive experiments show that both algorithms outperform state-of-the-art non-ensemble algorithms by a significant margin. Moreover, we compare and…
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Taxonomy
TopicsComplex Network Analysis Techniques · Text and Document Classification Technologies · Advanced Graph Neural Networks
