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洞見 - Hypergraph Representation Learning
Sampling-Efficient Hypergraph Self-Supervised Learning with Tri-Directional Signals
The proposed SE-HSSL framework introduces three sampling-efficient self-supervised signals - node-level CCA, group-level CCA, and hierarchical membership-level contrast - to effectively learn discriminative hypergraph representations without relying on arbitrary negative sampling.
Convolutional Signal Propagation: A Simple and Scalable Algorithm for Hypergraph Representation Learning
Convolutional Signal Propagation (CSP) is a simple and scalable algorithm for learning representations on hypergraphs, which can be applied to tasks such as node classification and retrieval.
