- Title
- Network embedding : taxonomies, frameworks and applications
- Creator
- Hou, Mingliang; Ren, Jing; Zhang, Da; Kong, Xiangjie; Zhang, Dongyu; Xia, Feng
- Date
- 2020
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/183691
- Identifier
- vital:16342
- Identifier
-
https://doi.org/10.1016/j.cosrev.2020.100296
- Identifier
- ISBN:1574-0137 (ISSN)
- Abstract
- Networks are a general language for describing complex systems of interacting entities. In the real world, a network always contains massive nodes, edges and additional complex information which leads to high complexity in computing and analyzing tasks. Network embedding aims at transforming one network into a low dimensional vector space which benefits the downstream network analysis tasks. In this survey, we provide a systematic overview of network embedding techniques in addressing challenges appearing in networks. We first introduce concepts and challenges in network embedding. Afterwards, we categorize network embedding methods using three categories, including static homogeneous network embedding methods, static heterogeneous network embedding methods and dynamic network embedding methods. Next, we summarize the datasets and evaluation tasks commonly used in network embedding. Finally, we discuss several future directions in this field. © 2020 Elsevier Inc.
- Publisher
- Elsevier Ireland Ltd
- Relation
- Computer Science Review Vol. 38, no. (2020), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2020 Elsevier Inc.
- Rights
- Open Access
- Subject
- 46 Information and Computing Sciences; Dynamics; Heterogeneity; Network embedding; Network science
- Full Text
- Reviewed
- Funder
- This work is partially supported by National Natural Science Foundation of China under Grant No. 61872054 and the Fundamental Research Funds for the Central Universities ( DUT19LAB23).
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