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A comprehensive survey of graph neural networks for knowledge graphs
The Knowledge graph, a multi-relational graph that represents rich factual information
among entities of diverse classifications, has gradually become one of the critical tools for …
among entities of diverse classifications, has gradually become one of the critical tools for …
[HTML][HTML] Graph attention networks: a comprehensive review of methods and applications
Real-world problems often exhibit complex relationships and dependencies, which can be
effectively captured by graph learning systems. Graph attention networks (GATs) have …
effectively captured by graph learning systems. Graph attention networks (GATs) have …
Contrastive meta learning with behavior multiplicity for recommendation
A well-informed recommendation framework could not only help users identify their
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …
Cross-domain recommendation via user interest alignment
Cross-domain recommendation aims to leverage knowledge from multiple domains to
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …
Contextualized knowledge graph embedding for explainable talent training course recommendation
Learning and development, or L&D, plays an important role in talent management, which
aims to improve the knowledge and capabilities of employees through a variety of …
aims to improve the knowledge and capabilities of employees through a variety of …
To see further: Knowledge graph-aware deep graph convolutional network for recommender systems
Applying a graph convolutional network (GCN) or its variants to user-item interaction graphs
is one of the most commonly used approaches for learning the representation of users and …
is one of the most commonly used approaches for learning the representation of users and …
Pre-training graph neural networks for link prediction in biomedical networks
Motivation Graphs or networks are widely utilized to model the interactions between different
entities (eg proteins, drugs, etc.) for biomedical applications. Predicting potential …
entities (eg proteins, drugs, etc.) for biomedical applications. Predicting potential …
Collaborative sequential recommendations via multi-view gnn-transformers
Sequential recommendation systems aim to exploit users' sequential behavior patterns to
capture their interaction intentions and improve recommendation accuracy. Existing …
capture their interaction intentions and improve recommendation accuracy. Existing …
Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks
Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for
studying gene expression patterns at the single-cell level. Inferring gene regulatory networks …
studying gene expression patterns at the single-cell level. Inferring gene regulatory networks …
Expgcn: Review-aware graph convolution network for explainable recommendation
Existing works in recommender system have widely explored extracting reviews as
explanations beyond user–item interactions, and formulated the explanation generation as a …
explanations beyond user–item interactions, and formulated the explanation generation as a …