Hierarchical Tensor Clustering for Multiple Graphs Representation

Graph clustering is a challenging task, especially when there is a hierarchical structure. The availability of multiple graphs (or relational graphs), in the multi-graph setting, provides additional information that can be leveraged to improve clustering results. This paper aims to develop a new hierarchical clustering algorithm for multi-graphs, the HTGM algorithm. This algorithm represents the set of graphs in the multi-graph as a 3-way tensor, and maximizes a modularity measure, extending the modularity-based graph clustering algorithm to multi-graphs and tensor structures. We evaluate the proposed algorithm over synthetic and real-world datasets and show the effectiveness of the proposed algorithm by benchmarking it to alternative clustering algorithms.