Graph tokenizations for Transformers induce distinct depth regimes with proven separations and impossibility results for converting between them at limited depth.
Graph-bert: Only attention is needed for learning graph representations
8 Pith papers cite this work. Polarity classification is still indexing.
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ST-TGExplainer disentangles stability and transition patterns in temporal graphs via a self-explainable TGNN guided by a disentangled information bottleneck objective to produce more faithful explanations.
SAGE is a self-evolving agentic graph-memory engine that dynamically constructs and refines structured memory graphs via writer-reader feedback, yielding performance gains on multi-hop QA, open-domain retrieval, and long-term agent benchmarks.
DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.
GraphDC applies divide-and-conquer multi-agent LLM reasoning to graph algorithms by decomposing graphs into subgraphs for local agents and integrating via a master agent, outperforming direct methods especially on large scales.
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
neuroGravity reconstructs transferable human mobility networks from basic urban data via physics-informed deep learning, with transferability predicted by a spatial income segregation index.
RL-SPH is an RL-based start primal heuristic that learns to turn infeasible starting points into feasible integer linear programming solutions, including problems with non-binary integers.
citing papers explorer
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Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers
Graph tokenizations for Transformers induce distinct depth regimes with proven separations and impossibility results for converting between them at limited depth.
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ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
ST-TGExplainer disentangles stability and transition patterns in temporal graphs via a self-explainable TGNN guided by a disentangled information bottleneck objective to produce more faithful explanations.
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SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory
SAGE is a self-evolving agentic graph-memory engine that dynamically constructs and refines structured memory graphs via writer-reader feedback, yielding performance gains on multi-hop QA, open-domain retrieval, and long-term agent benchmarks.
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DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs
DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.
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GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning
GraphDC applies divide-and-conquer multi-agent LLM reasoning to graph algorithms by decomposing graphs into subgraphs for local agents and integrating via a master agent, outperforming direct methods especially on large scales.
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Attention-based graph neural networks: a survey
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
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Transferable Human Mobility Network Reconstruction with neuroGravity
neuroGravity reconstructs transferable human mobility networks from basic urban data via physics-informed deep learning, with transferability predicted by a spatial income segregation index.
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RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs
RL-SPH is an RL-based start primal heuristic that learns to turn infeasible starting points into feasible integer linear programming solutions, including problems with non-binary integers.