- Title
- Graph Force Learning
- Creator
- Sun, Ke; Liu, Jiaying; Yu, Shuo; Xu, Bo; Xia, Feng
- Date
- 2020
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176658
- Identifier
- vital:15165
- Identifier
-
https://doi.org/10.1109/BigData50022.2020.9378120
- Identifier
- ISBN:9781728162515 (ISBN)
- Abstract
- Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning. © 2020 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 8th IEEE International Conference on Big Data, Big Data 2020 p. 2987-2994
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2020 IEEE
- Rights
- Open Access
- Subject
- Graph visualization; Label prediction; Network feature learning; Spring-electrical model
- Full Text
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