# Multimodal Clustering for Community Detection

## TL;DR

This paper advances object-attribute biclustering techniques for mining multi-mode communities in social network analysis, demonstrating their effectiveness and scalability in large real-world multi-mode networks.

## Contribution

It presents recent developments in OA-biclustering, extending it to multi-mode community detection and connecting it with known clustering coefficients in social network analysis.

## Key findings

- OA-biclustering effectively detects communities in multi-mode networks
- The method scales well to large real-world datasets
- OA-bicluster density correlates with clustering coefficients in SNA

## Abstract

Multimodal clustering is an unsupervised technique for mining interesting patterns in $n$-adic binary relations or $n$-mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed $n$-sets for $n$-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or 2-mode networks) arose by the end of the last decade due to intractability of computation problems related to formal concepts; this type of patterns was proposed as a meaningful and scalable approximation of formal concepts. In this paper, our aim is to present recent advance in OA-biclustering and its extensions to mining multi-mode communities in SNA setting. We also discuss connection between clustering coefficients known in SNA community for 1-mode and 2-mode networks and OA-bicluster density, the main quality measure of an OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks show that this type of patterns is suitable for community detection in multi-mode cases within reasonable time even though the number of corresponding $n$-cliques is still unknown due to computation difficulties. An interpretation of OA-biclusters for 1-mode networks is provided as well.