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Found 4 papers, 3 papers with code
Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis
Finally, SEA-A introduces a new evaluation metric called mismatch score to assess the consistency between paper contents and reviews.
A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
In this survey, we comprehensively review existing works on learning from graphs with heterophily.
Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks
We first introduce asymmetric graph contrastive learning for pretext to address heterophily and align the objectives of pretext and downstream tasks.
Empower Text-Attributed Graphs Learning with Large Language Models (LLMs)
In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs.
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