通过NLP技术与最大团枚举实现高模ularity图划分
High-Modularity Graph Partitioning Through NLP Techniques and Maximal Clique Enumeration
AI总结：
本文提出Clique-TF-IDF框架，利用NLP技术与最大团枚举实现高模ularity图划分，实验结果优于现有算法。
摘要：
自然语言处理（NLP）提供了强大的工具用于解读和处理人类语言，具有广泛的应用前景。在本文中，我们通过应用NLP技术计算词频和逆文档频率（TF-IDF）以及机器学习聚类算法，解决一个经典的组合问题——寻找高模ularity图划分。我们提出了一种新的框架，称为Clique-TF-IDF，用于图划分任务，该任务在各种网络分析场景中具有重要意义。该方法利用图中的密集子结构，即最大团，以类似术语-文档矩阵的方式表示每个顶点所参与的团。实验表明，Clique-TF-IDF的结果与或优于当前最先进的算法相当，无论是否事先知道划分数量。尽管该框架强调团和划分，但可以扩展为设计AI驱动的解决方案，以解决能够高效枚举子结构的各类具有挑战性的组合问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
