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Attraction and Repulsion: Unsupervised Domain Adaptive Graph Contrastive Learning Network
Graph convolutional networks (GCNs) are important techniques for many graph related analytics tasks. To date, most GCNs are designed …
Learning multi-level weight-centric features for few-shot learning
Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. …
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GCNFusion: An efficient graph convolutional network based model for information diffusion
Investigating the dynamics of spreading processes in real-world applications such as pathogen spread prediction, marketing, political …
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Discrete Embedding for Attributed Graphs
Attributed graphs refer to graphs where both node links and node attributes are observable for analysis. Attributed graph embedding …
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Anomaly Detection in Dynamic Graphs via Transformer
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks,e-commerce, and …
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One-shot Learning-based Animal Video Segmentation
Deep learning-based video segmentation methods can offer good performance after being trained on the large-scale pixel labeled …
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Deep Learning Data Augmentation for Raman Spectroscopy Cancer Tissue Classification
Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS …
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Learning Graph Representations with Maximal Cliques
Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional …
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Deep Neighbor-aware Embedding for Node Clustering in Attributed Graphs
Node clustering aims to partition the vertices in a graph into multiple groups or communities. Existing studies have mostly focused on …
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A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal …
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