Presents GraphInfer-Bench to demonstrate that no evaluated LLM-based method family closes the performance gap on graph inference tasks requiring multi-node reasoning, with plain GNNs matching or exceeding them.
Let your graph do the talking: Encoding structured data for llms
12 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
12
Pith papers citing it
7
external citations · external index
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representative citing papers
MixRAGRec is a multi-agent KG-RAG framework with an MoE retrieval agent for query-specific granularity, a knowledge alignment agent, and a contrastive recommendation agent trained jointly via MMAPO.
KoRe compresses one-hop knowledge-graph subgraphs into 20 discrete tokens that, injected into Qwen3-8B, match or beat text-based knowledge injection on three QA benchmarks while using up to 10x fewer tokens.
UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.
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.
ALL-IN projects node features to a random shared space and uses covariance operators to produce representations invariant to input feature permutations and orthogonal transformations, enabling transfer across graph datasets.
RelAgent uses an LLM agent to autonomously generate SQL feature programs paired with classical models for interpretable relational learning predictions that execute efficiently on standard databases.
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
LLMs using few-shot in-context learning on serialized k-hop subgraphs from synthetic AML scenarios can assess suspiciousness and generate natural-language justifications.
LGPT and Early Query Fusion create flexible graph representations for LLMs, achieving 4.13% improvement on GraphQA without training the model.
A survey of LLMs for graph computation introduces a role-based taxonomy of executors versus planners and concludes that current models suit simple small-scale tasks but remain unreliable for large-scale exact computation.
CureLLM adds curvature-aware edge modeling and prompt-based alignment to graph LLMs, claiming to resolve over-squashing from negative curvature and outperforming 20 baselines on 11 datasets.
citing papers explorer
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GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs
Presents GraphInfer-Bench to demonstrate that no evaluated LLM-based method family closes the performance gap on graph inference tasks requiring multi-node reasoning, with plain GNNs matching or exceeding them.
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Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation
MixRAGRec is a multi-agent KG-RAG framework with an MoE retrieval agent for query-specific granularity, a knowledge alignment agent, and a contrastive recommendation agent trained jointly via MMAPO.
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KoRe: Compact Knowledge Representations for Large Language Models
KoRe compresses one-hop knowledge-graph subgraphs into 20 discrete tokens that, injected into Qwen3-8B, match or beat text-based knowledge injection on three QA benchmarks while using up to 10x fewer tokens.
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A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning
UniGraphLM uses a multi-domain multi-task GNN encoder and adaptive alignment to create unified graph tokens for LLMs across diverse domains and tasks.
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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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Bridging Input Feature Spaces Towards Graph Foundation Models
ALL-IN projects node features to a random shared space and uses covariance operators to produce representations invariant to input feature permutations and orthogonal transformations, enabling transfer across graph datasets.
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RelAgent: LLM Agents as Data Scientists for Relational Learning
RelAgent uses an LLM agent to autonomously generate SQL feature programs paired with classical models for interpretable relational learning predictions that execute efficiently on standard databases.
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Heterogeneous Scientific Foundation Model Collaboration
Eywa enables language-based agentic AI systems to collaborate with specialized scientific foundation models for improved performance on structured data tasks.
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Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs
LLMs using few-shot in-context learning on serialized k-hop subgraphs from synthetic AML scenarios can assess suspiciousness and generate natural-language justifications.
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Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models
LGPT and Early Query Fusion create flexible graph representations for LLMs, achieving 4.13% improvement on GraphQA without training the model.
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Are Large Language Models Suitable for Graph Computation? Progress and Prospects
A survey of LLMs for graph computation introduces a role-based taxonomy of executors versus planners and concludes that current models suit simple small-scale tasks but remain unreliable for large-scale exact computation.
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Edge-Aware Curvature Modeling for Graph Understanding in Large Language Models
CureLLM adds curvature-aware edge modeling and prompt-based alignment to graph LLMs, claiming to resolve over-squashing from negative curvature and outperforming 20 baselines on 11 datasets.