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Found 9 papers, 5 papers with code
Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
To integrate text information, various methods have been introduced, mostly following a naive fusion framework.
Mixture of Link Predictors
Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning.
Drag-A-Video: Non-rigid Video Editing with Point-based Interaction
In this paper, we propose a new diffusion-based method for interactive point-based video manipulation, called Drag-A-Video.
Augment with Care: Enhancing Graph Contrastive Learning with Selective Spectrum Perturbation
Existing augmentation views with perturbed graph structures are usually based on random topology corruption in the spatial domain; however, from perspectives of the spectral domain, this approach may be ineffective as it fails to pose tailored impacts on the information of different frequencies, thus weakening the agreement between the augmentation views.
Label-free Node Classification on Graphs with Large Language Models (LLMS)
In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs).
Alternately Optimized Graph Neural Networks
Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.
