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Found 10 papers, 5 papers with code
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning
Inspired by this, we introduce Graph-aware Parameter-Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning with LLMs on text-rich graphs.
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
We demonstrate in our experiments that SymGNN can significantly improve the energy barrier prediction over other GNNs and non-graph machine learning models.
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high-school subjects and are confined to elementary algebraic operations.
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data.
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction
However, GNN explanation for link prediction (LP) is lacking in the literature.
Linkless Link Prediction via Relational Distillation
In this work, to combine the advantages of GNNs and MLPs, we start with exploring direct knowledge distillation (KD) methods for link prediction, i. e., predicted logit-based matching and node representation-based matching.
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
Explaining machine learning models is an important and increasingly popular area of research interest.
Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation
Conversely, multi-layer perceptrons (MLPs) have no graph dependency and infer much faster than GNNs, even though they are less accurate than GNNs for node classification in general.
Ranked #3 on
Node Classification
on AMZ Computers
Graph Condensation for Graph Neural Networks
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns.
Motif-Driven Contrastive Learning of Graph Representations
Our framework MotIf-driven Contrastive leaRning Of Graph representations (MICRO-Graph) can: 1) use GNNs to extract motifs from large graph datasets; 2) leverage learned motifs to sample informative subgraphs for contrastive learning of GNN.
