Hypergraph representations
5 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Hypergraph representations
Most implemented papers
HNHN: Hypergraph Networks with Hyperedge Neurons
Hypergraphs provide a natural representation for many real world datasets.
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
yy-ko/cash
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To tackle both challenges together, in this paper, we propose a novel hyperedge prediction framework (CASH) that employs (1) context-aware node aggregation to precisely capture complex relations among nodes in each hyperedge for (C1) and (2) self-supervised contrastive learning in the context of hyperedge prediction to enhance hypergraph representations for (C2).
Hypergraph Contrastive Learning for Drug Trafficking Community Detection
GraphResearcher/HyGCL-DC
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2023 IEEE International Conference on Data Mining (ICDM) 2023
To this end, we propose a novel HyperGraph Contrastive Learning framework called HyGCL-DC that employs hypergraph to model the higher-order relationships among users to detect Drug trafficking Communities.
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
kswoo97/hypeboy
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Based on the generative SSL task, we propose a hypergraph SSL method, HypeBoy.
Dual-level Hypergraph Contrastive Learning with Adaptive Temperature Enhancement
However, these works have the following limitations in modeling the high-order relationships over unlabeled data: (i) They primarily focus on maximizing the agreements among individual node embeddings while neglecting the capture of group-wise collective behaviors within hypergraphs; (ii) Most of them disregard the importance of the temperature index in discriminating contrastive pairs during contrast optimization.
