23 papers · ranked by Valyu relevance
S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das
—Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as triad counts is essential, as hypergraphs can reveal intricate group interaction patterns that conventional graphs fail to capture. In realworld scenarios, these…
Marco Bressan, Stefano Clemente, Giacomo Fumagalli
We study the problem of counting $k$-\emph{hyper}graphlets, an interesting but surprisingly ignored primitive, with the aim of understanding if efficient algorithms exist. To this end we consider \emph{color coding}, a well-known technique for approximately counting $k$-graphlets in graphs. Our first result is that, on…
Hanyu Xie, Changjian Song, Hao Shao, Lunwen Wang + 1 more
Hyperedge prediction is crucial for uncovering higher-order relationships in complex systems but faces core challenges, including unmodeled node influence heterogeneity, overlooked hyperedge order effects, and data sparsity. This paper proposes Order propagation Fusion Self-supervised learning for Hyperedge prediction…
Antoine Amarilli, Mikaël Monet, Rémi de Pretto
In this note, we study two rewrite rules on hypergraphs, called edge-domination and node-domination, and show that they are confluent. These rules are rather natural and commonly used before computing the minimum hitting sets of a hypergraph. Intuitively, edge-domination allows us to remove hyperedges that are…
Yazeed Alkhrijah, Abbas N. Talib, Narinderjit Singh Sawaran Singh, Ashraf Abed Hussein
Food recommendation systems face fundamental challenges in modeling the complex, compositional relationships among users, foods, and ingredients. Traditional collaborative filtering and Graph Neural Networks rely on pairwise connections that oversimplify culinary interactions, while existing hypergraph approaches use…
Mario Pascual-Gonzalez, Ezequiel Lopez-Rubio
We present IsalHG, a method for representing the structure of any finite, connected hypergraph of bounded hyperedge arity as a string over a compact instruction alphabet $Σ_{\mathrm{HG}}$. The encoding is executed by a small virtual machine comprising a sparse hypergraph, a circular doubly-linked list (CDLL) of node…
Meilin Liu, Wenping Zheng, Shuxia Yuan, Jeng-Shyang Pan + 3 more
Hypergraph neural networks have shown strong potential for node classification due to their ability to capture high-order relationships and multi-granularity structural patterns. However, real-world hypergraphs are often sparse, which limits interaction modeling through node-hyperedge incidence and, in turn, weakens…
Pengtao Dang, Paveethran Swaminathan, Tingbo Guo, Changlin Wan + 2 more
The exponential trajectory of biomedical literature has precipitated a fundamental “synthesis gap” in metabolic research, where critical mechanistic insights remain fragmented across hundreds of thousands of disjointed full-text articles, preventing the consolidation of a global mechanistic view. Here, we present…
Jiangnan Zhou, Sheng Zhang, Bing Wu, Qiuming Wang + 5 more
Existing methods for hypergraph node classification usually rely on local message passing and use a unified strategy for topological modeling across hyperedges of different sizes. However, they have two limitations in semi-supervised settings. First, representation learning mainly depends on local neighborhoods, making…
Mengyao Zhou, Zhiheng Zhou, Xiao Han, Guiying Yan
Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships. However, their performance is highly dependent on labeled data, making them vulnerable to label noise. Despite advances in learning with label noise (LLN) and graph learning with label noise…
Md Ishtyaq Mahmud, Tania Banerjee
We propose HyperNiche, a hypergraph-based framework for modeling higher-order, heterogeneous cellular niches from spatial transcriptomics data. Unlike conventional graph-based methods that rely on pairwise similarity and tend to produce homogeneous clusters, HyperNiche learns anchor-centered hyperedges through a…
Amir Hassan Shariatmadari, Sikun Guo, Nathan C. Sheffield, Aidong Zhang + 1 more
Research in scientific domains now generates more than a million articles annually, overwhelming researchers and hindering discovery. This surge has sparked interest in biomedical hypothesis generation (HG), which aims to uncover implicit patterns among biomedical concepts. Most existing methods focus on pairwise link…
He Jialin, Popescu, Nicholas, Zhu Chun-jiang
We initiate the study on fault-tolerant spanners in hypergraphs and develop fast algorithms for their constructions. A fault-tolerant (FT) spanner preserves approximate distances under network failures, often used in applications like network design and distributed systems. While classic (fault-free) spanners are…
Jiyun Lee, Jihyo Lee
Mutation-specific therapeutic vulnerabilities remain difficult to identify in precision oncology because lineage effects and network topology can obscure true synthetic lethal relationships. Here, we present a computational framework that maps genomic mutational profiles and genome-wide CRISPR-Cas9 screens onto…
Shuaihua Chen, Teng Chen, Zhikun Xu, Lujia Zhang + 2 more
Genome-scale metabolic models are essential for understanding cellular metabolism, yet existing deep learning approaches remain black boxes, and traditional flux balance analysis (FBA) cannot provide sample-specific predictions. To our knowledge, CytoGem-XAI is the first framework to combine hypergraph neural network…
Authors not listed
Conventional molecular graphs often are unable to reliably encode stereochemistry, especially for symmetric molecules, non-tetrahedral centers, and transition states. To overcome this, we present StereoMolGraph, an open source Python library implementing a stereochemistry-aware graph representation for molecules and…
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This paper investigates several chemical systems through the lens of hyperstructures and superhyperstructures. We first review Chemical HyperStructures and Chemical SuperHyperStructures defined by redox-driven hyperoperations on species sets with maximal electromotive force selection. Building on the general (m,n)-…
Fabio Cumbo, Kabir Dhillon, M. Hassan Najafi, Sercan Aygun + 1 more
The exponential growth of genomic databases necessitates alignment-free methods for comparing genomes. While MinHash-based tools have revolutionized this field by efficiently estimating the Average Nucleotide Identity based on k-mer sets, they inherently discard structural genomic information. We introduce HyperSketch…
Authors not listed
The Polytope Formalism provides a rigorous and unifying mathematical framework for representing all possible molecular configurations and their interrelationships. Extending its application from stereoisomerism to molecular constitution reveals that both arise from a common structural foundation linking discrete and…
Yael Hodaya Moshe, Mini Sharma, Anat Dahan, Hila Gvirts
Despite the growing use of functional near-infrared spectroscopy (fNIRS) hyperscanning to record brain activity simultaneously from interacting individuals in naturalistic settings, most analyses quantify functional connectivity separately for each channel pair. The resulting collection of pairwise estimates is…
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
Authors not listed
We present a new method for fingerprint- ing atomic configurations relevant to ML-IAM training and application, utilizing the ChIMES descriptor. These fingerprints enable rigor- ous analysis of statistical distinguishability be- tween configurations. Sample applications in- clude assessing diversity within ML-IAP…
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Topological band theory based on the variety of reciprocal space invariants provides an insightful framework for quantum material characterization in condensed matter physics. Its bulk-boundary correspondence principle has become a reliable tool for predicting Fermi level properties. Here, we investigate a recently…