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Multi-site Hyper-graph Convolutional Neural Networks and Application [PDF]
Jisuanji kexue, 2022 Recently,the exploitation of graph neural networks for neurological brain disorder diagnosis has attracted much attention.However,the graphs used in the existing studies are usually based on the pairwise connections of different nodes,and thus cannot ...
ZHOU Hai-yu, ZHANG Dao-qiang
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The treewidth of 2-section of hypergraphs [PDF]
Discrete Mathematics & Theoretical Computer Science, 2021 Let $H=(V,F)$ be a simple hypergraph without loops. $H$ is called linear if $|f\cap g|\le 1$ for any $f,g\in F$ with $f\not=g$. The $2$-section of $H$, denoted by $[H]_2$, is a graph with $V([H]_2)=V$ and for any $ u,v\in V([H]_2)$, $uv\in E([H]_2)$ if ...
Ke Liu, Mei Lu
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Quasirandomness in hypergraphs [PDF]
Electronic Notes in Discrete Mathematics, 2017 An $n$-vertex graph $G$ of edge density $p$ is considered to be quasirandom if it shares several important properties with the random graph $G(n,p)$. A well-known theorem of Chung, Graham and Wilson states that many such `typical' properties are asymptotically equivalent and, thus, a graph $G$ possessing one such property automatically satisfies the ...
Aigner-Horev, E +4 more
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, 2021 In this paper, we prove that for any $k\ge 3$, there exist infinitely many minimal asymmetric $k$-uniform hypergraphs. This is in a striking contrast to $k=2$, where it has been proved recently that there are exactly $18$ minimal asymmetric graphs. We also determine, for every $k\ge 1$, the minimum size of an asymmetric $k$-uniform hypergraph.
Yiting Jiang +2 more
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Intersections of hypergraphs [PDF]
Journal of Combinatorial Theory, Series B, 2015 Given two weighted k-uniform hypergraphs G, H of order n, how much (or little) can we make them overlap by placing them on the same vertex set? If we place them at random, how concentrated is the distribution of the intersection? The aim of this paper is to investigate these questions.
Bollobas, B, Scott, A
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Be More with Less: Hypergraph Attention Networks for Inductive Text Classification [PDF]
Conference on Empirical Methods in Natural Language Processing, 2020 Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this ...
Kaize Ding +4 more
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High-Quality Hypergraph Partitioning [PDF]
ACM Journal of Experimental Algorithmics, 2021 Hypergraphs are a generalization of graphs where edges (aka nets) are allowed to connect more than two vertices. They have a similarly wide range of applications as graphs.
Sebastian Schlag +5 more
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HEGEL: Hypergraph Transformer for Long Document Summarization [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 Extractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization.
Haopeng Zhang, Xiao Liu, Jiawei Zhang
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Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach
AAAI Conference on Artificial Intelligence, 2021 Quantitative trading and investment decision making are intricate financial tasks that rely on accurate stock selection. Despite advances in deep learning that have made significant progress in the complex and highly stochastic stock prediction problem ...
Ramit Sawhney +4 more
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Even order uniform hypergraph via the Einstein product
AKCE International Journal of Graphs and Combinatorics, 2023 We propose the algebraic connectivity of an undirected 2m-uniform hypergraph under the Einstein product. We generalize the algebraic connectivity to a directed 2m-uniform hypergraph and reveal the relationship between the vertex connectivity and the ...
Jiaqi Gu, Yimin Wei
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