Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs | Paper Citation Record

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Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs GPT-4 Technical Report

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Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs Spectral Networks and Locally Connected Networks on Graphs

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Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs Out-of-Distribution Detection on Graphs: A Survey

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Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs Label-free Node Classification on Graphs with Large Language Models (LLMS)

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Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning

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This paper cites TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models.

Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models

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Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

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Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs One for All: Towards Training One Graph Model for All Classification Tasks

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