Embedding-based Silhouette Community Detection

Abstract

Mining complex data in the form of networks is of increasing interest in many scientific disciplines. Network communities correspond to densely connected subnetworks, and often represent key functional parts of real-world systems. In this work, we propose Silhouette Community Detection (SCD), an approach for detecting communities, based on clustering of network node embeddings, i.e. real valued representations of nodes derived from their neighborhoods. We investigate the performance of the proposed SCD approach on 234 synthetic networks, as well as on a real-life social network. Even though SCD is not based on any form of modularity optimization, it performs comparably or better than state-of-the-art community detection algorithms, such as the InfoMap and Louvain algorithms. Further, we demonstrate how SCD's outputs can be used along with domain ontologies in semantic subgroup discovery, yielding human-understandable explanations of communities detected in a real-life protein interaction network. Being embedding-based, SCD is widely applicable and can be tested out-of-the-box as part of many existing network learning and exploration pipelines.

References in corpus (23)

• Distributed Representations of Words and Phrases and their Compositionality
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• DeepWalk: Online Learning of Social Representations
• Semi-Supervised Classification with Graph Convolutional Networks
• Finding community structure in very large networks
• Natural Scales in Geographical Patterns
• LINE: Large-scale Information Network Embedding
• Inductive Representation Learning on Large Graphs
• Benchmark graphs for testing community detection algorithms
• Resolution limit in community detection
• Community detection algorithms: a comparative analysis
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
• Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
• struc2vec: Learning Node Representations from Structural Identity
• The performance of modularity maximization in practical contexts
• The map equation
• PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
• Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
• A Comparative Analysis of Community Detection Algorithms on Artificial Networks
• Generalized Louvain Method for Community Detection in Large Networks
• Fast community detection by SCORE
• Poincaré Embeddings for Learning Hierarchical Representations
• The many facets of community detection in complex networks

Cited by in corpus (4)

• Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media
• CARL-G: Clustering-Accelerated Representation Learning on Graphs
• SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations
• Beyond One Solution: The Case for a Comprehensive Exploration of Solution Space in Community Detection