Community detection in networks using graph embeddings

TL;DR

This paper evaluates the effectiveness of graph embedding methods for community detection, comparing them to traditional algorithms, and finds that embeddings do not currently offer a clear advantage due to parameter sensitivity and computational costs.

Contribution

The study systematically compares graph embedding techniques with traditional community detection methods, highlighting their limitations and the challenges in parameter selection.

Findings

Embedding performance is comparable to traditional methods with proper parameters.

Optimal parameters vary with graph features, complicating real-world application.

Embedding techniques are computationally costly and do not outperform existing algorithms.

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.