arXiv AI
AnchorRM: Parallel Intra-Batch State Reconstruction with Anchor-Based Residual Memory for Temporal Graph Neural Networks
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arXiv Machine Learning
Aug 14
MARCH: Scaling Recurrent Memory with Content-Routed State Anchors
arXiv:2608. 12435v1 Announce Type: new Abstract: Transformers owe much of their strong long-context retrieval capability to a token-level memory that grows with context length.
arXiv Machine Learning
22h ago
MambaTG: Candidate-Aware State Space Modeling for Future Link Prediction on Temporal Graphs
MambaTG is a new framework for future link prediction on temporal graphs that addresses both repeated and unseen edge prediction. It leverages two complementary signals: the historical-sequence evolution of a node’s interactions and the compatibility between a candidate edge and the node’s evolving history. By using a candidate-aware Mamba architecture, the model captures ordered interaction dynamics and adapts state transitions to different candidates, achieving strong performance across 13 datasets while remaining computationally efficient.
arXiv Machine Learning
Oct 16
HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning
HERO is a heterogeneous continual graph learning framework that preserves historical knowledge at both structural and semantic levels while adapting to new tasks. It uses DiSCo to select representative target nodes and reconstruct compact heterogeneous contexts, and applies multi-level knowledge distillation to align task-conditioned predictions with semantic responses. Experiments on four datasets with three HGNN backbones show HERO achieves best or tied-best average performance while maintaining competitive forgetting.
Hugging Face Trending Papers
1d ago
MambaTG: Candidate-Aware State Space Modeling for Future Link Prediction on Temporal Graphs
Future link prediction on temporal graphs involves both repeated interactions between previously connected node pairs and first-time interactions between pairs with no prior interaction. While repeate...
arXiv AI
Sep 10
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
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.