Pure concatenation of LLM features degrades GNN accuracy on homophilous datasets, with Delta_sig metric predicting when the drop occurs better than homophily.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2representative citing papers
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
citing papers explorer
-
LLM Features Can Hurt GNNs: Concatenation Interference on Homophilous Graph Benchmarks
Pure concatenation of LLM features degrades GNN accuracy on homophilous datasets, with Delta_sig metric predicting when the drop occurs better than homophily.
-
Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.