The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Dynamic Network Embedding by Modeling Triadic Closure Process
Open accessAbstract
Network embedding, which aims to learn the low-dimensional representations of vertices, is an important task and has attracted considerable research efforts recently. In real world, networks, like social network and biological networks, are dynamic and evolving over time. However, almost all the existing network embedding methods focus on static networks while ignore network dynamics. In this paper, we present a novel representation learning approach, DynamicTriad, to preserve both structural information and evolution patterns of a given network. The general idea of our approach is to impose triad, which is a group of three vertices and is one of the basic units of networks. In particular, we model how a closed triad, which consists of three vertices connected with each other, develops from an open triad that has two of three vertices not connected with each other. This triadic closure process is a fundamental mechanism in the formation and evolution of networks, thereby makes our model being able to capture the network dynamics and to learn representation vectors for each vertex at different time steps. Experimental results on three real-world networks demonstrate that, compared with several state-of-the-art techniques, DynamicTriad achieves substantial gains in several application scenarios. For example, our approach can effectively be applied and help to identify telephone frauds in a mobile network, and to predict whether a user will repay her loans or not in a loan network.
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Cites
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Cited by
Network Representation Learning: A Survey
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Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs
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Self-citationTemporal Graph Networks for Deep Learning on Dynamic Graphs
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020) · arXiv · doi:10.48550/arxiv.2006.10637 · Cited by 382
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks
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Representation learning for dynamic graphs: A survey
Kazemi, S. M., Goel, R., Jain, K., Kobyzev, I., Sethi, A., Forsyth, P., & Poupart, P. (2019) · arXiv · doi:10.48550/arxiv.1905.11485 · Cited by 338
Metadata
- Authors
- Zhou, L., Yang, Y., Ren, X., Wu, F., & Zhuang, Y.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-25
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2787927827
- Cited by
- 473
- References
- 34
- FWCI
- 25.70
- Type
- conference paper
- Language
- en
- Open access
- diamond
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