Ensemble-based Deep Multilayer Community Search
Jianwei Wang, Yuehai Wang, Kai Wang, Xuemin Lin, Wenjie Zhang, Ying, Zhang

TL;DR
EnMCS is an innovative ensemble-based framework for multilayer community search that combines layer-shared and layer-specific information in an unsupervised manner, outperforming existing methods in efficiency and effectiveness.
Contribution
It introduces a novel unsupervised ensemble approach with HoloSearch and EMerge components for flexible, layer-aware community detection in multilayer graphs.
Findings
EnMCS outperforms existing methods in efficiency and effectiveness.
HoloSearch effectively captures layer-specific and shared features.
EMerge accurately synthesizes communities across layers.
Abstract
Multilayer graphs, consisting of multiple interconnected layers, are widely used to model diverse relationships in the real world. A community is a cohesive subgraph that offers valuable insights for analyzing (multilayer) graphs. Recently, there has been an emerging trend focused on searching query-driven communities within the multilayer graphs. However, existing methods for multilayer community search are either 1) rule-based, which suffer from structure inflexibility; or 2) learning-based, which rely on labeled data or fail to capture layer-specific characteristics. To address these, we propose EnMCS, an Ensemble-based unsupervised (i.e., label-free) Multilayer Community Search framework. EnMCS contains two key components, i.e., HoloSearch which identifies potential communities in each layer while integrating both layer-shared and layer-specific information, and EMerge which is an…
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Taxonomy
TopicsComplex Network Analysis Techniques · Web Data Mining and Analysis · Expert finding and Q&A systems
