Search Results for author:
Found 12 papers, 6 papers with code
Can Large Language Models Act as Ensembler for Multi-GNNs?
The model first aligns multiple GNNs, mapping the representations of different GNNs into the same space.
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning
The advent of the "pre-train, prompt" paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in Natural Language Processing (NLP).
Boosting Graph Foundation Model from Structural Perspective
The model constructs virtual super nodes to unify structural characteristics of graph data from different domains.
Improving Graph Out-of-distribution Generalization on Real-world Data
In contrast to previous studies that impose rigid independence assumptions on environments and invariant sub-graphs, this paper presents the theorems of environment-label dependency and mutable rationale invariance, where the former characterizes the usefulness of environments in determining graph labels while the latter refers to the mutable importance of graph rationales.
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.
Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes
Instead of directly reconstructing raw features for attributed nodes, GraMI generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes.
Resist Label Noise with PGM for Graph Neural Networks
To further leverage the information of clean labels in the noisy label set, we put forward LNP-v2, which incorporates the noisy label set into the Bayesian network to generate clean labels.
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.
Context-aware Session-based Recommendation with Graph Neural Networks
Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session.
Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples
In addition, we propose a variant model AdaMEOW that adaptively learns soft-valued weights of negative samples to further improve node representation.
