Affiliation:
1. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR
Abstract
Dynamic graphs serve as a generic abstraction and description of the evolutionary behaviors of various complex systems (e.g., social networks and communication networks). Temporal link prediction (TLP) is a classic yet challenging inference task on dynamic graphs, which predicts possible future linkage based on historical topology. The predicted future topology can be used to support some advanced applications on real-world systems (e.g., resource pre-allocation) for better system performance. This survey provides a comprehensive review of existing TLP methods. Concretely, we first give the formal problem statements and preliminaries regarding data models, task settings, and learning paradigms that are commonly used in related research. A hierarchical fine-grained taxonomy is further introduced to categorize existing methods in terms of their data models, learning paradigms, and techniques. From a generic perspective, we propose a unified encoder-decoder framework to formulate all the methods reviewed, where different approaches only differ in terms of some components of the framework. Moreover, we envision serving the community with an open-source project
OpenTLP
1
that refactors or implements some representative TLP methods using the proposed unified framework and summarizes other public resources. As a conclusion, we finally discuss advanced topics in recent research and highlight possible future directions.
Funder
Research Grants Council of Hong Kong under the Research Impact Fund
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Cited by
3 articles.
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1. Machine Learning on Dynamic Graphs: A Survey on Applications;2023 IEEE Ninth Multimedia Big Data (BigMM);2023-12-11
2. Exhaustive Evaluation of Dynamic Link Prediction;2023 IEEE International Conference on Data Mining Workshops (ICDMW);2023-12-04
3. RaftGP: Random Fast Graph Partitioning;2023 IEEE High Performance Extreme Computing Conference (HPEC);2023-09-25