- Title
- TOSNet : a topic-based optimal subnetwork identification in academic networks
- Creator
- Bedru, Hayat; Zhao, Wenhong; Alrashoud, Mubarak; Tolba, Amr; Guo, He; Xia, Feng
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176333
- Identifier
- vital:15095
- Identifier
-
https://doi.org/10.1109/ACCESS.2020.3034997
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic- aware subnetwork identification is essential to discover potential researchers on particular research topics and provide qualitywork. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 8, no. (2020), p. 201015-201027
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2021 by the authors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Rights
- Open Access
- Subject
- 08 Information and Computing Sciences; 09 Engineering; 10 TechnologyAcademic social networks; Collaboration intensity; Network science; Subnetwork identifi- cation; Subnetwork ranking; Topic modeling
- Full Text
- Reviewed
- Funder
- This work was supported in part by the International Scientific Partnership Program (ISPP) at King Saud University under Grant ISPP-78, and in part by the Zhejiang Provincial Fundamental Public Welfare Research Program under Grant LGG18E050025.
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