High-Modularity Graph Partitioning Through NLP Techniques and Maximal Clique Enumeration

TL;DR

This paper introduces Clique-TF-IDF, a novel NLP-inspired framework for high-modularity graph partitioning that leverages maximal cliques and machine learning, achieving competitive or superior results to existing methods.

Contribution

The paper presents a new graph partitioning framework combining NLP techniques and maximal clique enumeration, advancing state-of-the-art performance without prior knowledge of the number of partitions.

Findings

Clique-TF-IDF outperforms existing algorithms in graph partitioning tasks.

The framework effectively utilizes maximal cliques to represent vertices.

Results are comparable or better than current state-of-the-art methods.

Abstract

Natural Language Processing (NLP) provides highly effective tools for interpreting and handling human language, offering a broad spectrum of applications. In this paper, we address a classic combinatorial problem -- finding graph partitions with high modularity -- by applying NLP techniques that compute term frequency and inverse document frequency (TF-IDF) alongside machine learning clustering algorithms. We present a new framework, called Clique-TF-IDF, designed for graph partitioning, a task that holds significant relevance across various network analysis contexts. This approach uses dense substructures of the graph, specifically maximal cliques, to represent each vertex in terms of the cliques it is part of, in a manner akin to term-document matrices. Experiments show that Clique-TF-IDF yields results that are comparable to or outperform the current state-of-the-art algorithms, whether or not the number of partitions is known in advance. Although this framework emphasizes on cliques and partitioning, it can be extended to devise AI-driven solutions for a variety of challenging combinatorial problems that can leverage efficiently enumerable substructures.