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A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
ImprovedGCN: An efficient and accurate recommendation system employing lightweight graph convolutional networks in social media
Abstract Graph Convolutional Networks (GCNs) have emerged as a hot topic of interest for
collaborative filtering among researchers in the recent past. The research which exists in …
collaborative filtering among researchers in the recent past. The research which exists in …
Addressing the impact of localized training data in graph neural networks
In the realm of Graph Neural Networks (GNNs), which excel at capturing intricate
dependencies in graph-structured data, we address a significant limitation. Most state-of-the …
dependencies in graph-structured data, we address a significant limitation. Most state-of-the …
[HTML][HTML] Multi-stream graph attention network for recommendation with knowledge graph
Z Hu, F **a - Journal of Web Semantics, 2024 - Elsevier
A bstract In recent years, the powerful modeling ability of Graph Neural Networks (GNNs)
has led to their widespread use in knowledge-aware recommender systems. However …
has led to their widespread use in knowledge-aware recommender systems. However …
[PDF][PDF] Graph-based Explainable Recommendation Systems: Are We Rigorously Evaluating Explanations?
In recent years, we have witnessed an increase in the amount of published research in the
field of Explainable Recommender Systems. These systems are designed to help users find …
field of Explainable Recommender Systems. These systems are designed to help users find …
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification
Graph Neural Networks (GNNs) have emerged as powerful tools for predicting outcomes in
graph-structured data. However, a notable limitation of GNNs is their inability to provide …
graph-structured data. However, a notable limitation of GNNs is their inability to provide …
Conditional Shift-Robust Conformal Prediction for Graph Neural Network
Graph Neural Networks (GNNs) have emerged as potent tools for predicting outcomes in
graph-structured data. Despite their efficacy, a significant drawback of GNNs lies in their …
graph-structured data. Despite their efficacy, a significant drawback of GNNs lies in their …
GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models
Graph Neural Networks (GNNs) have increasingly become an indispensable tool in learning
from graph-structured data, catering to various applications including social network …
from graph-structured data, catering to various applications including social network …
Increasing the Effectiveness of Personalized Recommender Systems Based on the Integrated GNN-RL Model
A modern approach to personalized recommendation systems is presented, combining
graph neural networks GNN with RL reinforcement learning methods. The GNN model is …
graph neural networks GNN with RL reinforcement learning methods. The GNN model is …