Self Weighted Multiplex Modularity Maximization for Multiview Clustering

In response to the challenge of representing data from multiple sources, researchers have proposed the use of multiplex graphs as a solution. Multiplex graphs are particularly useful for representing multi-view data, where each layer represents a specific type of interaction. Pillar community detection of multiplex graphs is a clustering application that computes groups of vertices across all layers. Modularity maximization is a popular technique for graph clustering, which has been generalized to multiplex graphs. However, this generalization did not consider the importance of each layer in pillar clustering. This paper presents a new technique called Self Weighted Multiplex Modularity (SWMM), which optimizes the weights associated with each layer and the partition that maximizes the multiplex modularity. The paper proposes two optimization methods, iterative and direct, and demonstrates the effectiveness and robustness of the technique in accurately retrieving clusters even when data is highly missing.