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Found 9 papers, 4 papers with code
Covering a Graph with Dense Subgraph Families, via Triangle-Rich Sets
A fundamental task in graph mining is to discover these dense subgraphs.
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks
In this paper, we investigate how LLMs can be leveraged in a computationally efficient fashion to benefit rich graph-structured data, a modality relatively unexplored in LLM literature.
An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce
This lack of interpretability hinders the development and adoption of new techniques in the field.
Communication-Free Distributed GNN Training with Vertex Cut
Here, we introduce CoFree-GNN, a novel distributed GNN training framework that significantly speeds up the training process by implementing communication-free training.
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You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction
We identify this setting as Graph Intersection-induced Transfer Learning (GITL), which is motivated by practical applications in e-commerce or academic co-authorship predictions.
Search Behavior Prediction: A Hypergraph Perspective
With these hyperedges, we augment the original bipartite graph into a new \textit{hypergraph}.
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks
Extensive evaluation of TuneUp on five diverse GNN architectures, three types of prediction tasks, and both transductive and inductive settings shows that TuneUp significantly improves the performance of the base GNN on tail nodes, while often even improving the performance on head nodes.
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
We propose Cold Brew, a teacher-student distillation approach to address the SCS and noisy-neighbor challenges for GNNs.
Task-Agnostic Graph Neural Explanations
TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models.
