Konstantinos Sismanis, Petros Potikas, Dora Souliou, Aris Pagourtzis
{"title":"利用图注意网络检测重叠群落","authors":"Konstantinos Sismanis, Petros Potikas, Dora Souliou, Aris Pagourtzis","doi":"10.1016/j.future.2024.107529","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107529"},"PeriodicalIF":6.2000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Overlapping community detection using graph attention networks\",\"authors\":\"Konstantinos Sismanis, Petros Potikas, Dora Souliou, Aris Pagourtzis\",\"doi\":\"10.1016/j.future.2024.107529\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":55132,\"journal\":{\"name\":\"Future Generation Computer Systems-The International Journal of Escience\",\"volume\":\"163 \",\"pages\":\"Article 107529\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future Generation Computer Systems-The International Journal of Escience\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167739X2400493X\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X2400493X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
Overlapping community detection using graph attention networks
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.
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.