arXiv:2608. 04377v1 Announce Type: cross Abstract: Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships.
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arXiv:2601. 17469v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics.
The paper introduces an unsupervised hypergraph neural network designed to detect anomalous hyperedges—higher-order associations that deviate from typical patterns. Unlike conventional graph methods that capture only pairwise relationships, this approach leverages hypergraphs to model associations among any number of entities. Experiments on real-life datasets show the model effectively identifies unusual hyperedges without requiring labeled data.
arXiv:2608. 15055v1 Announce Type: new Abstract: Hypergraphs effectively model higher-order groupwise relationships beyond pairwise interactions, while pretrained language models (PLMs) and large language models (LLMs) provide rich semantic understanding from textual attributes.
Particle Competition and Cooperation for Robust Graph Convolutional Network Learning Under Label Noise proposes PCC+GCN, a hybrid framework that refines labels using Particle Competition and Cooperation before training a GCN. PCC identifies suspicious nodes via particle domination dynamics and decides whether to keep, remove, or reassign their labels, optionally augmenting the graph with k‑nearest‑neighbor edges. Evaluated on ten NoisyGL datasets under various noise types, PCC+GCN achieved the highest average accuracy and rank, outperforming baseline GCN by 1.67 percentage points and proving computationally efficient, especially under instance‑dependent noise.
arXiv:2606. 09051v1 Announce Type: new Abstract: Convolutions have successfully transitioned from image processing to the complex realm of non-Euclidean higher-order domains, particularly in hypergraphs.
arXiv:2406.04805v4 Announce Type: replace-cross Abstract: The rapid adoption, usefulness, and resource-intensive training of Graph Neural Network (GNN) models have made them an invaluable intellectua...