Results 11 to 20 of about 56,309 (276)
On the spectrum of hypergraphs
Linear Algebra and its Applications, 2016 Here we study the spectral properties of an underlying weighted graph of a non-uniform hypergraph by introducing different connectivity matrices, such as adjacency, Laplacian and normalized Laplacian matrices. We show that different structural properties
Chris Ritchie (1952305) +4 more
core +5 more sources
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. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise formulation.
S. Bai, Feihu Zhang, Philip H. S. Torr
semanticscholar +5 more sources
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
openaire +2 more sources
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
openaire +3 more sources
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
semanticscholar +1 more source
A Survey on Hypergraph Representation Learning
ACM Computing Surveys, 2023 Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts.
Alessia Antelmi +5 more
semanticscholar +1 more source
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation [PDF]
International Conference on Information and Knowledge Management, 2021 With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the
Junwei Zhang +5 more
semanticscholar +1 more source
Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image
Remote Sensing, 2021 Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information.
Jinhuan Xu, Liang Xiao, Jingxiang Yang
doaj +1 more source
Spatio-Temporal Hypergraph Learning for Next POI Recommendation
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023 Next Point-of-Interest (POI) recommendation task focuses on predicting the immediate next position a user would visit, thus providing appealing location advice.
Xiaodong Yan +6 more
semanticscholar +1 more source
Hypergraph Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2018 In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.
Yifan Feng +4 more
semanticscholar +1 more source