Interrelated Dense Pattern Detection in Multilayer Networks

doi:10.1109/TKDE.2024.3398683

	Interrelated Dense Pattern Detection in Multilayer Networks
	Feng, Wenjie 1,2; Wang, Li 3,4; Hooi, Bryan 2; Ng, See Kiong 2; Liu, Shenghua 3,4
	2024-11-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	36 期号:11 页码:6462-6476
摘要	Given a heterogeneous multilayer network with various connections in pharmacology, how can we detect components with intensive interactions and strong dependencies? Can we accurately capture suspicious groups in a multi-lot transaction network under camouflage? These challenges related to dense subgraph detection have been extensively studied in simple graphs (such as bipartite graph, multi-view network) but remain under-explored on complex networks. Existing methods struggle to effectively handle the intricate dependencies, let alone accurately identify the interrelated dense connected patterns within a series of complex heterogeneous networks. In this paper, we propose InDuen, a novel algorithm designed to detect interrelated densest subgraphs in multilayer networks through joint optimization of coupled factorization and local search for an elaborate-designed joint density measure. It is (a) effective for both large synthetic and real networks, (b) resistant to camouflage for anomaly detection, and (c) linearly scalable. Experimental results demonstrate that InDuen outperforms the state-of-the-art baselines in accurately detecting interrelated densest subgraphs under various settings. Furthermore, InDuen uncovers some intriguing patterns in real-world data, i.e., closely cooperated academic groups and interrelated dependent functional components in biology-net. InDuen achieves more than 35x speedup compared to the SOTA method Destine.
关键词	Nonhomogeneous media Matrix decomposition Diseases Cross layer design Image edge detection Task analysis Optimization Multilayer network dense subgraph detection interrelated pattern mining algorithm design
DOI	10.1109/TKDE.2024.3398683
收录类别	SCI
语种	英语
资助项目	National Science Foundation of China[U21B2046] ; National Science Foundation of China[6237075198] ; National Research Foundation Singapore ; DSO National Laboratories through AI Singapore Programme (AISG)[AISG2-RP-2020-018] ; National Key R&D Program of China[2023YFC3305303]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001336378400129
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41169
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Feng, Wenjie; Liu, Shenghua
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Natl Univ Singapore, Inst Data Sci, Singapore 117602, Singapore 3.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab AI Secur, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Feng, Wenjie,Wang, Li,Hooi, Bryan,et al. Interrelated Dense Pattern Detection in Multilayer Networks[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2024,36(11):6462-6476.
APA	Feng, Wenjie,Wang, Li,Hooi, Bryan,Ng, See Kiong,&Liu, Shenghua.(2024).Interrelated Dense Pattern Detection in Multilayer Networks.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,36(11),6462-6476.
MLA	Feng, Wenjie,et al."Interrelated Dense Pattern Detection in Multilayer Networks".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 36.11(2024):6462-6476.