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Community-Based Network Alignment for Large Attributed Network
Conference proceeding

Community-Based Network Alignment for Large Attributed Network

Zheng Chen, Xinli Yu, Bo Song, Jianliang Gao, Xiaohua Hu, Wei-Shih Yang and Assoc Comp Machinery
CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, v 131841, pp 587-596
01 Jan 2017
DOI: https://doi.org/10.1145/3132847.3132904

Abstract

Computer Science Computer Science, Information Systems Computer Science, Theory & Methods Science & Technology Technology
Network alignment is becoming an active topic in network data analysis. Despite extensive research, we realize that efficient use of topological and attribute information for large attributed network alignment has not been sufficiently addressed in previous studies. In this paper, based on Stochastic Block Model (SBM) and Dirichlet-multinomial, we propose "divide-and-conquer" models CAlign that jointly consider network alignment, community discovery and community alignment in one framework for large networks with node attributes, in an effort to reduce both the computation time and memory usage while achieving better or competitive performance. It is provable that the algorithms derived from our model have sub-quadratic time complexity and linear space complexity on a network with small densification power, which is true for most real-world networks. Experiments show CAlign is superior to two recent state-of-art models in terms of accuracy, time and memory on large networks, and CAlign is capable of handling millions of nodes on a modern desktop machine.

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19 citations in Web of Science
21 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Information Systems
Computer Science, Theory & Methods
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