Feb 19, 2024 · In this work, we focus on synergizing LLMs and graph models with their complementary strengths by distilling the power of LLMs to a local graph model on TAG ...
Aug 6, 2024 · Graph models can efficiently learn TAGs, but their training heavily relies on human-annotated labels, which are scarce or even unavailable in ...
A curated list of papers and resources about large language models (LLMs) on graphs based on our survey paper: Large Language Models on Graphs: A Comprehensive ...
Aug 6, 2024 · This work explores an innovative approach to combining the strengths of large language models and graph models for the task of learning from text-attributed ...
In this repository, we collect papers focusing on LLM for graph learning tasks at node, edge and graph levels.
Jan 24, 2024 · In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two ...
Compared to teacher LLM, distilled GNN achieves superior inference speed equipped with much fewer computing and storage demands, when surpassing the teacher ...
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Jun 6, 2024 · This method integrates the rich textual and visual data of molecules with the structural analysis power of GNNs. Extensive experiments reveal ...
May 21, 2024 · o First Step: Asking LLMs to automatically generate the context of the input graph. o Second Step: The generated new context is merged with the ...
Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph ...