Results 61 to 70 of about 47,538 (182)
Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022 Session-based recommendation (SBR) aims at the next-item prediction with a short behavior session. Existing solutions fail to address two main challenges: 1) user interests are shown as dynamically coupled intents, and 2) sessions always contain noisy ...
Yinfeng Li +4 more
semanticscholar +1 more source
A Vulnerability Lens for Intuitive‐Logic Scenarios
FUTURES &FORESIGHT SCIENCE, Volume 8, Issue 1, April 2026.ABSTRACT Exploration of possibilities by means of intuitive logic is hampered by a large number of scenarios, which easily exceed the limits imposed by human bounded rationality. While many practitioners constrain their scenarios within a 2 × 2 $2\times 2$ matrix by design, more structured approaches point to rationales such as eliminating ...
Guido Fioretti
wiley +1 more source
The Largest Laplacian and Signless Laplacian H-Eigenvalues of a Uniform Hypergraph [PDF]
, 2013 In this paper, we show that the largest Laplacian H-eigenvalue of a $k$-uniform nontrivial hypergraph is strictly larger than the maximum degree when $k$ is even. A tight lower bound for this eigenvalue is given.
Hu, Shenglong, Qi, Liqun, Xie, Jinshan
core
Analysis of quantum error correction with symmetric hypergraph states
, 2018 Graph states have been used to construct quantum error correction codes for independent errors. Hypergraph states generalize graph states, and symmetric hypergraph states have been shown to allow for the correction of correlated errors. In this paper, it
Bruß, Dagmar +2 more
core +1 more source
, 2011 A support of a hypergraph H is a graph with the same vertex set as H in which each hyperedge induces a connected subgraph. We show how to test in polynomial time whether a given hypergraph has a cactus support, i.e. a support that is a tree of edges and cycles.
Brandes, Ulrik +3 more
openaire +3 more sources
Discrepancy of arithmetic progressions in boxes and convex bodies
Mathematika, Volume 72, Issue 2, April 2026.Abstract The combinatorial discrepancy of arithmetic progressions inside [N]:={1,…,N}$[N]:= \lbrace 1, \ldots, N\rbrace$ is the smallest integer D$D$ for which [N]$[N]$ can be colored with two colors so that any arithmetic progression in [N]$[N]$ contains at most D$D$ more elements from one color class than the other.
Lily Li, Aleksandar Nikolov
wiley +1 more source
Dynamic Hypergraph Neural Networks
International Joint Conference on Artificial Intelligence, 2019 In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model.
Jianwen Jiang +4 more
semanticscholar +1 more source
Large language models for bioinformatics
Quantitative Biology, Volume 14, Issue 1, March 2026.Abstract With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications.
Wei Ruan +54 more
wiley +1 more source
A comprehensive review of cluster methods for drug–drug interaction network
Quantitative Biology, Volume 14, Issue 1, March 2026.Abstract The detection of drug–drug interaction (DDI) is crucial to the rational use of drug combinations. Experimentally, DDI detection is time‐consuming and laborious. Currently, researchers have developed a variety of computational methods to predict DDI.
Shuyuan Cao +3 more
wiley +1 more source
Hypergraph Learning with Line Expansion
, 2020 Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss.
Abdelzaher, Tarek +3 more
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