arXiv AI
Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification
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SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning
SiST‑GNN introduces a simultaneous spatial‑temporal message‑passing framework for dynamic graph neural networks, fusing per‑node temporal embeddings with spatial aggregation in a single operation. By maintaining a recurrent hidden state per node and treating it as a cross‑time edge, the model jointly reasons over topology and evolution. Experiments on link‑prediction and node‑classification benchmarks show significant improvements over prior methods, achieving up to 158% gains in live‑update link prediction and outperforming discrete‑time baselines by 7–23% in dynamic node classification.
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TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs
TTGBench is a new benchmark for temporal graph learning that evaluates both structural evolution and semantic drift in text‑attributed graphs. It includes six real‑world, text‑rich datasets with dual volatility and supports multi‑class and multi‑label temporal node classification, addressing gaps left by existing benchmarks. A comprehensive evaluation of 17 state‑of‑the‑art methods shows a clear divide: TGNNs excel at structural prediction but struggle with semantic tracking, while LLM‑based models perform better on semantic tasks but lag in structural prediction.
arXiv Machine Learning
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NodeGround: A Node Classification Benchmark in the Graph Foundation Model Era
arXiv:2609.39673v1 Announce Type: new Abstract: Can a pretrained graph model replace training and tuning a separate predictor for each dataset? Answering this requires evaluating prediction quality a...
arXiv AI
Sep 16
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
GraphIFE addresses the class imbalance problem in graph-structured data by tackling a quality inconsistency issue in synthesized nodes. The framework uses graph invariant learning to strengthen embedding space representations and identify invariant features, leading to improved performance on minority classes. Experiments show that GraphIFE consistently outperforms various baselines across multiple datasets.
arXiv Machine Learning
Sep 23
CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning
CacheDyG introduces a cache‑refine framework that decouples temporal propagation from parameter updates in dynamic graph neural networks. By storing graph‑aware node‑time representations in non‑trainable buffers and updating only a lightweight refiner, residual gate, and link predictor during training, it reduces repeated recomputation of historical structures. Experiments on five benchmarks show that CacheDyG uses fewer trainable parameters, runs faster, and achieves competitive or better predictive performance compared to existing baselines.
arXiv AI
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STAMP: Symmetric Temporal Averaging Message Passing for Domain Adaptation in Chronologically Split Graphs
The paper introduces STAMP, a timestamp‑balanced message‑passing operator designed for domain adaptation in chronologically split graph data. STAMP replaces standard neighborhood aggregation with temporally aligned propagation, enforcing first‑moment invariance across time while remaining scalable and easy to implement. Experiments on several temporal graph benchmarks, including ogbn‑arxiv, ogbn‑mag, and a temporal‑split Pokec dataset, show that STAMP and its variant STAMP‑mag consistently improve node classification accuracy and achieve state‑of‑the‑art performance.