Community detection in networks using graph embeddings

Abstract

Graph embedding methods are becoming increasingly popular in the machine learning community, where they are widely used for tasks such as node classification and link prediction. Embedding graphs in geometric spaces should aid the identification of network communities as well, because nodes in the same community should be projected close to each other in the geometric space, where they can be detected via standard data clustering algorithms. In this paper, we test the ability of several graph embedding techniques to detect communities on benchmark graphs. We compare their performance against that of traditional community detection algorithms. We find that the performance is comparable, if the parameters of the embedding techniques are suitably chosen. However, the optimal parameter set varies with the specific features of the benchmark graphs, like their size, whereas popular community detection algorithms do not require any parameter. So it is not possible to indicate beforehand good parameter sets for the analysis of real networks. This finding, along with the high computational cost of embedding a network and grouping the points, suggests that, for community detection, current embedding techniques do not represent an improvement over network clustering algorithms.

arXiv comment16 pages, 13 figures

References in corpus (20)

• Efficient Estimation of Word Representations in Vector Space
• Fast unfolding of communities in large networks
• Community structure in social and biological networks
• Finding and evaluating community structure in networks
• Modularity and community structure in networks
• Community detection in graphs
• Maps of random walks on complex networks reveal community structure
• Near linear time algorithm to detect community structures in large-scale networks
• Benchmark graphs for testing community detection algorithms
• Resolution limit in community detection
• Community detection algorithms: a comparative analysis
• Graph Embedding Techniques, Applications, and Performance: A Survey
• Community detection in networks: A user guide
• Finding statistically significant communities in networks
• Spectral redemption: clustering sparse networks
• Consistency of community detection in networks under degree-corrected stochastic block models
• Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel
• Encoding dynamics for multiscale community detection: Markov time sweeping for the Map equation
• Estimating the resolution limit of the map equation in community detection
• Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs

Cited by in corpus (7)

• Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
• Zoo Guide to Network Embedding
• Robustness of community structure under edge addition
• Hippocluster: an efficient, hippocampus-inspired algorithm for graph clustering
• Iterative embedding and reweighting of complex networks reveals community structure
• Exact and rapid linear clustering of networks with dynamic programming
• Systematic comparison of graph embedding methods in practical tasks