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Graph Convolution Network (GCN) is a potent deep learning methodology and GCN-based graph data augmentation methods excel in recommender systems. The augmented data, as the higher-order collaborative signals, is generated by pre-training the GCN ...

Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep ...

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• Cancer Drug Response Prediction using multi-omics data of sole cancer genes with deep ResNet model.
• Tivozanib, SNX-2112, CGP-60474, PHA-665752 and Foretinib with low IC50 values were found to be effective anti-cancer drugs.
• Case ...

Graph Convolutional Networks (GCNs) have succeeded remarkably in skeleton-based action recognition tasks. However, the existing GCN-based methods, where the interframe edges of the graph connect only the same joints and ignore the correlations ...

Graph Convolutional Networks (GCNs) are pivotal in analyzing graph data. However, as graph complexity increases, heterophily challenges the traditional GCNs that rely on homophily assumptions. These challenges have elicited various mitigation ...

Auxiliary behavior data is introduced into multi-behavior recommendation (MB-Rec) to alleviate the recommendation data sparsity. Recent studies have shown that different auxiliary behaviors usually appear in a specific order (e.g., view > cart > ...

Group recommendation involves suggesting items or activities to a group of users based on their collective preferences or characteristics. Graph contrastive learning is a technique used to learn representations of items and users in a graph ...

Social media has evolved into a widely influential information source in contemporary society. However, the widespread use of social media also enables the rapid spread of fake news, which can pose a significant threat to national and social ...

• We propose a novel fake news detection model that combines the graph convolution network and BERT.
• We employ the co-attention mechanism to obtain better results and achieve training convergence in a shorter time.
• We conduct ...

Graph Neural Networks (GNNs) have opened up a potential line of research for collaborative filtering (CF). The key power of GNNs is based on injecting collaborative signal into user and item embeddings which will contain information about user-...

Graph Neural Network (GNN) is a promising technique in representation learning on graph data. Graph Convolution Network (GCN) and Graph Attention Network (GAT) are two main representative techniques in GNN, which can learn node embedding on the ...

The graph contrastive learning approach, which combines graph convolutional networks (GCNs) with contrastive learning, has been widely applied in recommender systems and achieved tremendous success. Most graph contrastive learning (GCL) methods ...

To explicitly exploit the collaborative signals in the user-item interaction graph, a growing number of recent Collaborative Filtering (CF) studies adopt Graph Convolution Network (GCN) as a basis. Though effective, these methods ...

To extract cues for pixelwise segmentation in an efficient way, this paper proposes a lightweight model that involves graph structure in the convolutional network. First, a cross-layer module is designed to adaptively aggregate hierarchical ...

Graph neural networks have shown good performance in many domains, as well as in combinatorial optimization. This paper proposes a new graph neural network framework to deal with the classical combinatorial optimization problem, the traveling ...

Diagnosing the contents of learning in brain activities is a long-standing research task in cognitive sciences. The current studies on cognitive diagnosis (CD) in education determine the status of knowledge concept (KC) based on the observed ...

With the advancement of technology, annotated multi-omics datasets are becoming increasingly abundant. In this paper, we propose a novel deep learning framework, called multi-omics data fusion network (MOFNet), to integrate multi-omics data for ...

Cluster-GCN is one of the effective methods for studying the scalability of Graph Neural Networks. The idea of this approach is to use METIS community detection algorithm to split the graph into several sub-graphs, or communities that are small ...

Vehicle re-identification (Re-ID) represents the task aiming to identify the same vehicle from images captured by different cameras. Recent years have seen various feature learning-based approaches merely focusing on feature representations including ...

Graph convolution neural network (GCN) has become a critical tool to capture representations of graph nodes. At present, the graph convolution model for large scale graphs is trained by full-batch stochastic gradient descent, which causes two ...

Video-based person re-identification aims to match the same identification from video clips captured by multiple non-overlapping cameras. By effectively exploiting both temporal and spatial clues of a video clip, a more comprehensive ...

In the context of machine learning on graph data, graph deep learning has captured the attention of many researcher. Due to the promising results of deep learning models in the most diverse fields of application, great efforts have been made to ...