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Hypergraph Convolution and Hypergraph Attention [PDF]
Pattern Recognition, 2019 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.
S. Bai, Feihu Zhang, Philip H. S. Torr
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Hypergraph Isomorphism Computation [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 The isomorphism problem, crucial in network analysis, involves analyzing both low-order and high-order structural information. Graph isomorphism algorithms focus on structural equivalence to simplify solver space, aiding applications like protein design,
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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Hypergraph Contrastive Collaborative Filtering [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022 Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data.
Lianghao Xia +5 more
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GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning [PDF]
Computer Vision and Pattern Recognition, 2022 Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only consider pair-wise interactions with limited relational reasoning. To promote
Chenxin Xu +4 more
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Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation [PDF]
Knowledge Discovery and Data Mining, 2022 Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations.
Yuhao Yang +5 more
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Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation [PDF]
AAAI Conference on Artificial Intelligence, 2020 Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role.
Xin Xia +5 more
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UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks [PDF]
International Joint Conference on Artificial Intelligence, 2021 Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains.
Jing Huang, Jie Yang
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Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023 Complexity science provides a powerful framework for understanding physical, biological and social systems, and network analysis is one of its principal tools. Since many complex systems exhibit multilateral interactions that change over time, in recent years, network scientists have become increasingly interested in modelling and ...
Corinna Coupette +2 more
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