Yanqiao Zhu · PaperMetrix

Deep Graph Structure Learning for Robust Representations: A Survey

2021

Graph Neural Networks (GNNs) are widely used for analyzing graph-structured data. Most GNN methods are highly sensitive to the quality of graph structures and usually require a perfect graph structure for learning informative embeddings. However, …

Neighborhood-Regularized Self-Training for Learning with Few Labels

2023 · arXiv (Cornell University)

Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of …

AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange

2023

Graph Neural Networks (GNNs) have already been widely used in various graph mining tasks. However, recent works reveal that the learned weights (channels) in well-trained GNNs are highly redundant, which inevitably limits the performance of …

Session-Based Recommendation with Graph Neural Networks

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising …

Deep Graph Contrastive Representation Learning

2020 · arXiv (Cornell University)

Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel framework for unsupervised graph representation learning by leveraging a contrastive objective …

Graph Contrastive Learning with Adaptive Augmentation