CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning | Paper Citation Record

Observation fe49612c-8849-47c6-a377-637595a9a56f · outbound

This paper cites Local homophily-aware graph neural network with adaptive polynomial filters for scalable graph anomaly detection.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Local homophily-aware graph neural network with adaptive polynomial filters for scalable graph anomaly detection

Reference 1

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Observation 1043ad29-249e-402f-a17a-692f01aff9ed · outbound

This paper cites Gcot: Chain-of- thought prompt learning for graphs.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Gcot: Chain-of- thought prompt learning for graphs

Reference 2

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This paper cites Gcc: Graph contrastive coding for graph neural network pre-training.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Gcc: Graph contrastive coding for graph neural network pre-training

Reference 3

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Observation 4ff9bd5f-9c3e-4499-9f05-1e55ace80f39 · outbound

This paper cites D-tracker: Modeling interest diffusion in social activity tensor data streams.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning D-tracker: Modeling interest diffusion in social activity tensor data streams

Reference 4

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This paper cites E-commerce search via content collaborative graph neural network.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning E-commerce search via content collaborative graph neural network

Reference 5

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This paper cites Gppt: Graph pre-training and prompt tuning to generalize graph neural networks.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Gppt: Graph pre-training and prompt tuning to generalize graph neural networks

Reference 6

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Observation 9a6a58aa-c804-41b3-b5b1-42c5f46367b9 · outbound

This paper cites Graph neural networks for multimodal single-cell data integration.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graph neural networks for multimodal single-cell data integration

Reference 7

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This paper cites Groot: Effective design of biological sequences with limited experimental data.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Groot: Effective design of biological sequences with limited experimental data

Reference 8

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Observation 481456b2-9405-44c6-bb4b-e9c9ddcdbb82 · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Semi-supervised classification with graph convolutional networks

Reference 9

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Observation efebc3cb-9bc8-4403-a5d1-58b4c552de38 · outbound

This paper cites Graph attention networks.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graph attention networks

Reference 10

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This paper cites Inductive representation learning on large graphs.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Inductive representation learning on large graphs

Reference 11

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This paper cites Deep graph infomax.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Deep graph infomax

Reference 12

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Observation 40635ac2-3a8f-419b-a705-fe43b6d1ebfe · outbound

This paper cites In Graph contrastive learning with augmentations, volume 33, pages 5812–5823, 2020.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning In Graph contrastive learning with augmentations, volume 33, pages 5812–5823, 2020

Reference 13

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This paper cites GraphPrompt: Unifying pre-training and downstream tasks for graph neural networks.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning GraphPrompt: Unifying pre-training and downstream tasks for graph neural networks

Reference 14

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Observation 7e0d784e-f95f-45e5-aa25-7e4900f2b778 · outbound

This paper cites Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs.IEEE TKDE, 2024.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs.IEEE TKDE, 2024

Reference 15

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Observation 68c14b6b-bec2-49c4-be19-6141bf5ffc87 · outbound

This paper cites A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning

Reference 16

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This paper cites Llaga: Large language and graph assistant.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Llaga: Large language and graph assistant

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This paper cites Graphgpt: Graph instruction tuning for large language models.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graphgpt: Graph instruction tuning for large language models

Reference 18

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Observation 3acb7747-9010-4b34-8e3f-5a62446f599d · outbound

This paper cites InChain-of-thought prompting elicits reasoning in large language models, volume 35, pages 24824–24837, 2022.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning InChain-of-thought prompting elicits reasoning in large language models, volume 35, pages 24824–24837, 2022

Reference 19

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Observation 4768693e-cc32-484c-b1fa-721d6f7bda13 · outbound

This paper cites InSelf-consistency improves chain of thought reasoning in language models, 2023.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning InSelf-consistency improves chain of thought reasoning in language models, 2023

Reference 20

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Observation 5b0860b9-18ae-49e6-ba6b-8864f90f1785 · outbound

This paper cites Graph chain-of-thought: Augmenting large language models by reasoning on graphs.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graph chain-of-thought: Augmenting large language models by reasoning on graphs

Reference 21

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This paper cites Extending the design space of graph neural networks by rethinking folklore weisfeiler-lehman.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Extending the design space of graph neural networks by rethinking folklore weisfeiler-lehman

Reference 22

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This paper cites Mag- gnn: Reinforcement learning boosted graph neural network.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Mag- gnn: Reinforcement learning boosted graph neural network

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This paper cites GNNBoundary: Towards explaining graph neural networks through the lens of decision boundaries.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning GNNBoundary: Towards explaining graph neural networks through the lens of decision boundaries

Reference 24

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This paper cites Improving graph neural networks by learning continuous edge directions.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Improving graph neural networks by learning continuous edge directions

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This paper cites Graph learning with distributional edge layouts.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graph learning with distributional edge layouts

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This paper cites Graph neural networks use graphs when they shouldn’t.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graph neural networks use graphs when they shouldn’t

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This paper cites How powerful are k-hop message passing graph neural networks.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning How powerful are k-hop message passing graph neural networks

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Observation ba949e2e-21a9-4203-8a02-e175e6626a32 · outbound

This paper cites How powerful are graph neural networks? InICLR, 2018.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning How powerful are graph neural networks? InICLR, 2018

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This paper cites On the bottleneck of graph neural networks and its practical implications.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning On the bottleneck of graph neural networks and its practical implications

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This paper cites Deep graph infomax.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Deep graph infomax

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graph contrastive learning automated

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graphmae: Self-supervised masked graph autoencoders

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Unigraph: Learning a unified cross-domain foundation model for text-attributed graphs

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graph neural prompting with large language models

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Large language model meets graph neural network in knowledge distillation

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Stage: Simplified text-attributed graph embeddings using pre-trained llms

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Login: A large language model consulted graph neural network training framework

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graph Prompt Learning: A Comprehensive Survey and Beyond

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Universal prompt tuning for graph neural networks

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graphtext: Graph reasoning in text space

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graphtranslator: Aligning graph model to large language model for open-ended tasks

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graph2text or graph2token: A perspective of large language models for graph learning.ACM, 2026

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Observation a603f6f7-72be-495e-98a9-da6a8f19115c · outbound

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Augmenting low-resource text classification with graph-grounded pre-training and prompting

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Graphclip: Enhancing transferability in graph foundation models for text-attributed graphs

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Llms as zero-shot graph learners: Alignment of gnn representations with llm token embeddings

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning HyperNetworks

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Node-time conditional prompt learning in dynamic graphs

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Open graph benchmark: Datasets for machine learning on graphs

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning

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This paper cites A comprehensive study on text-attributed graphs: Benchmarking and rethinking.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning A comprehensive study on text-attributed graphs: Benchmarking and rethinking

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Multilayer perceptron (mlp)

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Geometric knowledge distillation: Topology compression for graph neural networks

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Global-local graph neural networks for node-classification

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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Nodeformer: A scalable graph structure learning transformer for node classification

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Resolution: unresolved; no resolver link, observed 2026-08-02T14:39:20.914934Z

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Observation 41420acd-2f8d-4d16-aab6-2b75ff1ecb40 · outbound

This paper cites Difformer: Scalable (graph) transformers induced by energy constrained diffusion.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Difformer: Scalable (graph) transformers induced by energy constrained diffusion

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-02T14:39:21.018846Z

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Observation 4f418aff-7358-4d2c-b839-a7f3ac484c33 · outbound

This paper cites Gonzalez, Ion Stoica, and Eric P.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Gonzalez, Ion Stoica, and Eric P

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-02T14:39:21.045968Z

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Observation 4086161a-75f9-40ce-bffe-729cfef82a9d · outbound

This paper cites One for all: Towards training one graph model for all classification tasks.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning One for all: Towards training one graph model for all classification tasks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-02T14:39:21.136952Z

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Observation f2f66b4d-5f73-404d-8d58-ab66c6940521 · outbound

This paper cites Gofa: A generative one-for-all model for joint graph language modeling.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Gofa: A generative one-for-all model for joint graph language modeling

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-02T14:39:21.226980Z

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Observation ee5d20f8-e7be-4c42-8855-fd36625e97a5 · outbound

This paper cites Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-02T14:39:21.450160Z

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Observation d3e3dec4-e6c7-4a03-9e89-57711a251dcf · outbound

This paper cites Children.

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning Children

Reference 256

Resolution: malformed identifier; no resolver link, observed 2026-08-02T14:39:21.340130Z

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