High-Order Topology-Enhanced Graph Convolutional Networks for Dynamic Graphs Article Swipe
Jiawei Zhu
,
Bo Li
,
Zhenshi Zhang
,
Ling Zhao
,
Haifeng Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/sym14102218
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/sym14102218
Understanding the evolutionary mechanisms of dynamic graphs is crucial since dynamic is a basic characteristic of real-world networks. The challenges of modeling dynamic graphs are as follows: (1) Real-world dynamics are frequently characterized by group effects, which essentially emerge from high-order interactions involving groups of entities. Therefore, the pairwise interactions revealed by the edges of graphs are insufficient to describe complex systems. (2) The graph data obtained from real systems are often noisy, and the spurious edges can interfere with the stability and efficiency of models. To address these issues, we propose a high-order topology-enhanced graph convolutional network for modeling dynamic graphs. The rationale behind it is that the symmetric substructure in a graph, called the maximal clique, can reflect group impacts from high-order interactions on the one hand, while not being readily disturbed by spurious links on the other hand. Then, we utilize two independent branches to model the distinct influence mechanisms of the two effects. Learnable parameters are used to tune the relative importance of the two effects during the process. We conduct link predictions on real-world datasets, including one social network and two citation networks. Results show that the average improvements of the high-order enhanced methods are 68%, 15%, and 280% over the corresponding backbones across datasets. The ablation study and perturbation analysis validate the effectiveness and robustness of the proposed method. Our research reveals that high-order structures provide new perspectives for studying the dynamics of graphs and highlight the necessity of employing higher-order topologies in the future.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/sym14102218
- https://www.mdpi.com/2073-8994/14/10/2218/pdf?version=1666336299
- OA Status
- gold
- Cited By
- 9
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307171272
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307171272Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/sym14102218Digital Object Identifier
- Title
-
High-Order Topology-Enhanced Graph Convolutional Networks for Dynamic GraphsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-21Full publication date if available
- Authors
-
Jiawei Zhu, Bo Li, Zhenshi Zhang, Ling Zhao, Haifeng LiList of authors in order
- Landing page
-
https://doi.org/10.3390/sym14102218Publisher landing page
- PDF URL
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https://www.mdpi.com/2073-8994/14/10/2218/pdf?version=1666336299Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-8994/14/10/2218/pdf?version=1666336299Direct OA link when available
- Concepts
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Spurious relationship, Computer science, Pairwise comparison, Substructure, Theoretical computer science, Network topology, Topology (electrical circuits), Mathematics, Artificial intelligence, Machine learning, Combinatorics, Operating system, Structural engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 4, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
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40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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