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title
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2026-02-16 00:17:45
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HierGraph Semi-Supervised Hierarchical Graph Classification In this work, we study node classification in a hierarchical graph perspective which arises in many domains such as social network and document collection. In the hierarchical graph, each node is represented with one graph instance. We propose the Hierarchical Graph Mutual Information (HGMI) to model consistency among different levels of hierarchical graph and design two novel semi-supervised solutions named SEAL-C/AI for node labels are usually limited in node classification Motivation Method Key Results Code and Datasets Contributors References Motivation A node of a graph usually is linked with a real-world entity such as a user belong to a social network, or a document in one document citation network. It is novel and interesting to represent a node with graph instance which leads to a hierarchical graph perspective . However, there is a lack of theoretical guarantee and practical ...
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