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Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning

arXiv 2408.02704 ↗First posted 5 Aug 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Dynamic graphs (DG) are often used to describe evolving interactions between nodes in real-world applications. Temporal patterns are a natural feature of DGs and are also key to representation learning. However, existing dynamic GCN models are mostly composed of static GCNs and sequence modules, which results in the separation of spatiotemporal information and cannot effectively capture complex temporal patterns in DGs. To address this problem, this study proposes a spatial-temporal graph convolutional networks with diversified transformation (STGCNDT), which includes three aspects: a) constructing a unified graph tensor convolutional network (GTCN) using tensor M-products without the need to represent spatiotemporal information separately; b) introducing three transformation schemes in GTCN to model complex temporal patterns to aggregate temporal information; and c) constructing an ensemble of diversified transformation schemes to obtain higher representation capabilities. Empirical studies on four DGs that appear in communication networks show that the proposed STGCNDT significantly outperforms state-of-the-art models in solving link weight estimation tasks due to the diversified transformations.

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References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2018How Powerful are Graph Neural Networks?cited by 4,393
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  5. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 447
  6. 2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 143
  7. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  8. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  9. 2017Dynamic Graph Convolutional Networkscited by 83
  10. 2022Scalable Spatiotemporal Graph Neural Networkscited by 52
  11. 2021Learning Conjoint Attentions for Graph Neural Netscited by 26
  12. 2017AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoderscited by 20
7 more
  1. 2019Efficient Neural Interaction Function Search for Collaborative Filteringcited by 16
  2. 2020K-Core based Temporal Graph Convolutional Network for Dynamic Graphscited by 13
  3. 2022PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecastingcited by 13
  4. 2015Robust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)cited by 11
  5. 2024Tensor Graph Convolutional Network for Dynamic Graph Representation Learningcited by 8
  6. 2022Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendationcited by 4
  7. 2024An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysiscited by 3

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