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Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation
Class association embedding distills the global class-related features of all instances into a unified domain with well separation between classes.
Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering
Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention.
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
In this study, we present a novel molecular optimization paradigm, Graph Polish, which changes molecular optimization from the traditional "two-language translating" task into a "single-language polishing" task.
HopGAT: Hop-aware Supervision Graph Attention Networks for Sparsely Labeled Graphs
Due to the cost of labeling nodes, classifying a node in a sparsely labeled graph while maintaining the prediction accuracy deserves attention.
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