Sun, Hanlin, Jie, Wei, Wang, Zhongmin, Wang, Hai and Ma, Sugang (2020). Network Representation Learning Guided by Partial Community Structure. IEEE Access, 8 , pp. 46665-46681.
Abstract
Network Representation Learning (NRL) is an effective way to analyze large scale networks (graphs). In general, it maps network nodes, edges, subgraphs, etc. onto independent vectors in a low dimension space, thus facilitating network analysis tasks. As community structure is one of the most prominent mesoscopic structure properties of real networks, it is necessary to preserve community structure of networks during NRL. In this paper, the concept of k-step partial community structure is defined and two Partial Community structure Guided Network Embedding (PCGNE) methods, based on two popular NRL algorithms (DeepWalk and node2vec respectively), for node representation learning are proposed. The idea behind this is that it is easier and more cost-effective to find a higher quality 1-step partial community structure than a higher quality whole community structure for networks; the extracted partial community information is then used to guide random walks in DeepWalk or node2vec. As a result, the learned node representations could preserve community structure property of networks more effectively. The two proposed algorithms and six state-of-the-art NRL algorithms were examined through multi-label classification and (inner community) link prediction on eight synthesized networks: one where community structure property could be controlled, and one real world network. The results suggested that the two PCGNE methods could improve the performance of their own based algorithm significantly and were competitive for node representation learning. Especially, comparing against used baseline algorithms, PCGNE methods could capture overlapping community structure much better, and thus could achieve better performance for multi-label classification on networks that have more overlapping nodes and/or larger overlapping memberships.
Publication DOI: | https://doi.org/10.1109/ACCESS.2020.2978517 |
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Divisions: | ?? 50811700Jl ?? College of Engineering & Physical Sciences |
Additional Information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Uncontrolled Keywords: | Network embedding,community structure,link prediction,multi-label classification,network representation learning,partial community structure,General Computer Science,General Materials Science,General Engineering |
Publication ISSN: | 2169-3536 |
Last Modified: | 06 Aug 2024 08:47 |
Date Deposited: | 12 Mar 2020 09:15 |
Full Text Link: | |
Related URLs: |
https://ieeexpl ... cument/9025035/
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2020-03-17 |
Published Online Date: | 2020-03-05 |
Accepted Date: | 2020-03-01 |
Authors: |
Sun, Hanlin
Jie, Wei Wang, Zhongmin Wang, Hai ( ![]() Ma, Sugang |