- Title
- OFFER: A Motif Dimensional Framework for Network Representation Learning
- Creator
- Yu, Shuo; Xia, Feng; Xu, Jin; Chen, Zhikui; Lee, Ivan
- Date
- 2020
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/175182
- Identifier
- vital:14941
- Identifier
- https:/doi.org/10.1145/3340531.3417446
- Identifier
- ISBN:9781450368599 (ISBN)
- Abstract
- Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency. © 2020 ACM.
- Publisher
- Association for Computing Machinery
- Relation
- 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 p. 3349-3352
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright ACM
- Rights
- Open Access
- Subject
- Link prediction; Multivariate relationship; Network motif; Network representation learning
- Full Text
- Reviewed
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