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Hypergraph convolution and hypergraph attention [PDF]
Pattern Recognition, 2021 Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise formulation.
Song Bai, Feihu Zhang, Philip H.S. Torr
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Journal of Combinatorial Theory, Series B, 2021 The following very natural problem was raised by Chung and Erd s in the early 80's and has since been repeated a number of times. What is the minimum of the Tur n number $\text{ex}(n,\mathcal{H})$ among all $r$-graphs $\mathcal{H}$ with a fixed number of edges?
Matija Bucić +3 more
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Dual-branch differential channel hypergraph convolutional network for human skeleton based action recognition. [PDF]
PLoS ONEGraph Convolutional Networks (GCNs) perform well in skeleton action recognition tasks, but their pairwise node connections make it difficult to effectively model high-order dependencies between non-adjacent joints.
Dong Chen +4 more
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Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph [PDF]
Scientific ReportsIn recent years, hypergraph neural networks have achieved remarkable success in tasks such as node classification, link prediction, and graph classification, thanks to their powerful computational capabilities.
Libing Bai +4 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Cross-modal remote sensing (RS) image retrieval aims to retrieve RS images using other modalities (e.g., text) and vice versa. The relationship between objects in the RS image is complex, i.e., the distribution of multiple types of objects is uneven ...
Fanglong Yao +6 more
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Demonstration of hypergraph-state quantum information processing
Nature CommunicationsComplex entangled states are the key resources for measurement-based quantum computations, which is realised by performing a sequence of measurements on initially entangled qubits.
Jieshan Huang +9 more
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Contributions to Discrete Mathematics, 2023 This article presents an extension of the study of metric and partition dimension to hypergraphs. We give sharp lower bounds for the metric and partition dimension of hypergraphs in general and give exact values under specified conditions.
Imran Javaid +3 more
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Hypergraph Isomorphism Computation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 The isomorphism problem is a fundamental problem in network analysis, which involves capturing both low-order and high-order structural information. In terms of extracting low-order structural information, graph isomorphism algorithms analyze the structural equivalence to reduce the solver space dimension, which demonstrates its power in many ...
Yifan Feng +3 more
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Hypergraph Based Berge Hypergraphs [PDF]
Graphs and Combinatorics, 2021 Fix a hypergraph $\mathcal{F}$. A hypergraph $\mathcal{H}$ is called a {\it Berge copy of $\mathcal{F}$} or {\it Berge-$\mathcal{F}$} if we can choose a subset of each hyperedge of $\mathcal{H}$ to obtain a copy of $\mathcal{F}$. A hypergraph $\mathcal{H}$ is {\it Berge-$\mathcal{F}$-free} if it does not contain a subhypergraph which is Berge copy of $\
Balko, Martin +4 more
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Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation [PDF]
The Web Conference, 2021 Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences.
Junliang Yu +5 more
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