mlsanityData to 7 Oct 2026

A General Benchmark Framework is Dynamic Graph Neural Network Need

arXiv 2401.06559 ↗First posted 12 Jan 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Dynamic graph learning is crucial for modeling real-world systems with evolving relationships and temporal dynamics. However, the lack of a unified benchmark framework in current research has led to inaccurate evaluations of dynamic graph models. This paper highlights the significance of dynamic graph learning and its applications in various domains. It emphasizes the need for a standardized benchmark framework that captures temporal dynamics, evolving graph structures, and downstream task requirements. Establishing a unified benchmark will help researchers understand the strengths and limitations of existing models, foster innovation, and advance dynamic graph learning. In conclusion, this paper identifies the lack of a standardized benchmark framework as a current limitation in dynamic graph learning research . Such a framework will facilitate accurate model evaluation, drive advancements in dynamic graph learning techniques, and enable the development of more effective models for real-world applications.

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References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  4. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  5. 2016Variational Graph Auto-Encoderscited by 1,373
  6. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  7. 2020Inductive Representation Learning on Temporal Graphscited by 454
  8. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  9. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  10. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 188
  11. 2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 131
  12. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
9 more
  1. 2022Neighborhood-aware Scalable Temporal Network Representation Learningcited by 54
  2. 2023Deep Temporal Graph Clusteringcited by 28
  3. 2023Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignmentcited by 25
  4. 2021Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influencescited by 18
  5. 2023Reinforcement Graph Clustering with Unknown Cluster Numbercited by 16
  6. 2023Decoupled Graph Neural Networks for Large Dynamic Graphscited by 14
  7. 2023Towards Open Temporal Graph Neural Networkscited by 13
  8. 2023G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clusteringcited by 9
  9. 2023TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classificationcited by 9

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