Overlapping community detection using graph attention networks

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-09-21 DOI:10.1016/j.future.2024.107529

Konstantinos Sismanis, Petros Potikas, Dora Souliou, Aris Pagourtzis

引用次数: 0

Abstract

Community detection is a research area with increasing practical significance. Successful examples of its application are found in many scientific areas like social networks, recommender systems and biology. Deep learning has achieved many successes (Miotto et al., 2018; Voulodimos et al., 2018) on various graph related tasks and is recently used in the field of community detection, offering accuracy and scalability. In this paper, we propose a novel method called Attention Overlapping Community Detection (AOCD) a method that incorporates an attention mechanism into the well-known method called Neural Overlapping Community Detection (NOCD) (Shchur and Günnemann, 2019) to discover overlapping communities in graphs. We perform several experiments in order to evaluate our proposed method’s ability to discover ground truth communities. Compared to NOCD, increased performance is achieved in many cases.

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利用图注意网络检测重叠群落

社群检测是一个越来越具有实际意义的研究领域。在社交网络、推荐系统和生物学等许多科学领域都有成功的应用实例。深度学习在各种图相关任务上取得了许多成功（Miotto 等人，2018 年；Voulodimos 等人，2018 年），最近被用于社群检测领域，提供了准确性和可扩展性。在本文中，我们提出了一种名为 "注意力重叠群落检测（AOCD）"的新方法，这种方法将注意力机制融入了著名的 "神经重叠群落检测（NOCD）"方法（Shchur 和 Günnemann，2019 年），以发现图中的重叠群落。我们进行了多项实验，以评估我们提出的方法发现基本真实社群的能力。与 NOCD 相比，我们在很多情况下都提高了性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.