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Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer
Machine Learning · Submitted 20 Apr 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Comprehensive experiments on four real-world datasets demonstrate RSGT's superior performance in discrete dynamic graph representation learning, consistently outperforming existing methods in dynamic link prediction tasks.
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The abstract, sentence by sentence
Dynamic graph representation learning has emerged as a crucial research area, driven by the growing need for analyzing time-evolving graph data in real-world applications. While recent approaches leveraging recurrent neural networks (RNNs) and graph neural networks (GNNs) have shown promise, they often fail to adequately capture the impact of temporal edge states on inter-node relationships, consequently overlooking the dynamic changes in node features induced by these evolving relationships. Furthermore, these methods suffer from GNNs' inherent over-smoothing problem, which hinders the extraction of global structural features. To address these challenges, we introduce the Recurrent Structure-reinforced Graph Transformer (RSGT), a novel framework for dynamic graph representation learning. It first designs a heuristic method to explicitly model edge temporal states by employing different edge types and weights based on the differences between consecutive snapshots, thereby integrating varying edge temporal states into the graph's topological structure. We then propose a structure-reinforced graph transformer that captures temporal node representations encoding both graph topology and evolving dynamics through a recurrent learning paradigm, enabling the extraction of both local and global structural features. Comprehensive experiments on four real-world datasets demonstrate RSGT's superior performance in discrete dynamic graph representation learning, consistently outperforming existing methods in dynamic link prediction tasks.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Dynamic graph representation learning has emerged as a crucial research area, driven by the growing need for analyzing time-evolving graph data in real-world applications. | background 1.00 | 0.03 | 0.02 | asserted 1.00 |
| s1 | While recent approaches leveraging recurrent neural networks (RNNs) and graph neural networks (GNNs) have shown promise, they often fail to adequately capture the impact of temporal edge states on inter-node relationships, consequently overlooking the dynamic changes in node features induced by these evolving relationships. | gap 0.93 | 0.05 | 0.03 | asserted 0.93 |
| s2 | Furthermore, these methods suffer from GNNs' inherent over-smoothing problem, which hinders the extraction of global structural features. | gap 0.73 | 0.05 | 0.02 | asserted 1.00 |
| s3 | To address these challenges, we introduce the Recurrent Structure-reinforced Graph Transformer (RSGT), a novel framework for dynamic graph representation learning. | contribution 0.99 | 0.90 | 0.06 | asserted 0.99 |
| s4 | It first designs a heuristic method to explicitly model edge temporal states by employing different edge types and weights based on the differences between consecutive snapshots, thereby integrating varying edge temporal states into the graph's topological structure. | method 0.96 | 0.93 | 0.04 | asserted 1.00 |
| s5 | We then propose a structure-reinforced graph transformer that captures temporal node representations encoding both graph topology and evolving dynamics through a recurrent learning paradigm, enabling the extraction of both local and global structural features. | method 0.93 | 0.93 | 0.12 | asserted 1.00 |
| s6 | Comprehensive experiments on four real-world datasets demonstrate RSGT's superior performance in discrete dynamic graph representation learning, consistently outperforming existing methods in dynamic link prediction tasks. | result 1.00 | 0.96 | 0.85 | asserted 1.00 |
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The paper
Original abstract
Dynamic graph representation learning has emerged as a crucial research area, driven by the growing need for analyzing time-evolving graph data in real-world applications. While recent approaches leveraging recurrent neural networks (RNNs) and graph neural networks (GNNs) have shown promise, they often fail to adequately capture the impact of temporal edge states on inter-node relationships, consequently overlooking the dynamic changes in node features induced by these evolving relationships. Furthermore, these methods suffer from GNNs' inherent over-smoothing problem, which hinders the extraction of global structural features. To address these challenges, we introduce the Recurrent Structure-reinforced Graph Transformer (RSGT), a novel framework for dynamic graph representation learning. It first designs a heuristic method to explicitly model edge temporal states by employing different edge types and weights based on the differences between consecutive snapshots, thereby integrating varying edge temporal states into the graph's topological structure. We then propose a structure-reinforced graph transformer that captures temporal node representations encoding both graph topology and evolving dynamics through a recurrent learning paradigm, enabling the extraction of both local and global structural features. Comprehensive experiments on four real-world datasets demonstrate RSGT's superior performance in discrete dynamic graph representation learning, consistently outperforming existing methods in dynamic link prediction tasks.
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How the literature uses it
1 arXiv paper cite this in 6 sentences
Papers citing it · 3 sentences without a confident label
Contests it 1 paper
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Another drawback of these methods [ref] is that they use only sub-graphs to represent the local structure around a given node.
Supra-Laplacian Encoding for Transformer on Dynamic Graphs · Related work · contest 0.74
Compares with it 1 paper
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We obtain the final edge representation $𝐞_𝐮,𝐯∈ℝ^d$ by applying an average time-pooling operator and we compute the probability that the nodes $u_t+1$ and $v_t+1$ are connected with: [equation] differs from methods that enrich node and edge representations with pairwise information by sampling substructures around each node [ref] .
Supra-Laplacian Encoding for Transformer on Dynamic Graphs · Edge Representation with Cross-Attention · 0.87
Cites it as background 1 paper
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Among them, some preserve the local structure by computing attention only on direct neighbors [ref] , while others sample local spatio-temporal structures around nodes [ref] and perform fully-connected attention.
Supra-Laplacian Encoding for Transformer on Dynamic Graphs · Related work · 0.86
What it cites · 3 sentences without a confident label
Cites as background 1 paper
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As the number of nodes in a dynamic graph grows across time slices, DynGEM [ref] employs a deep autoencoder to progressively construct the representations of a particular snapshot from the embedding of the preceding snapshot.
DynGEM: Deep Embedding Method for Dynamic Graphs · Discrete Dynamic Graph Representation Learning · 0.80
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