16 papers · ranked by Valyu relevance
Muhammed Ali Pala, Muhammet Burhan Navdar, Jae-Ho Han
Background/Objectives: Traditional medical image analysis methods often suffer from locality bias, limiting their ability to model long-range contextual relationships between spatially distributed anatomical structures. To overcome this challenge, this study proposes SPX-GNN (Superpixel Explainable Graph Neural…
Zehan Li, Xuemeng Zhai, Hangyu Hu, Jiandong Liang + 2 more
Graph neural networks (GNNs) have achieved great success in graph classification, with graph pooling methods being widely adopted for related tasks. Existing approaches typically rely on node ranking or clustering to coarsen graphs, but often fail to effectively leverage global structural information, leading to loss…
Chaohui Zhen, Canhua Yao, Song Li, Zihong Lin + 4 more
Introduction Accurate classification of colonoscopic images is essential for early detection and characterization of colorectal diseases. Recent advances in deep learning, particularly transformer-based architectures and graph neural networks (GNNs), provide alternative strategies for modeling global contextual…
Amin Khodaei, Zahra Pourabbas, Fatemeh Hashem-zadehdizajyekan, Erfan Esmaeili
Title: Highlights 1. • Modeling the structure of various viruses in the form of directed weighted graphs. 2. • Presenting a feature extraction algorithm based on complex networks metrics. 3. • The impact of the eigen-vector, input centrality and output centrality measures of specific nucleotide triplets within genes…
Zhaoyang Wang, Xianghui Fu, Bo Deng, Yang Chen + 1 more
In algebraic topology, a k-dimensional simplex is defined as a convex polytope consisting of k + 1 vertices. If spatial dimensionality is not considered, it corresponds to the complete graph with k + 1 vertices in graph theory. The alternating sum of the number of simplices across dimensions yields a topological…
Niels Holtgrefe, Katharina T. Huber, Leo van Iersel, Mark Jones + 1 more
In evolutionary biology, phylogenetic networks are graphs that provide a flexible framework for representing complex evolutionary histories that involve reticulate evolutionary events. Recently, phylogenetic studies have started to focus on a special class of such networks called semi-directed networks. These graphs…
Fabio Cumbo, Kabir Dhillon, Jayadev Joshi, Davide Chicco + 2 more
Viral species classification is crucial for understanding viral evolution, epidemiology, and developing effective diagnostics and treatments. Traditional methods often rely on sequence similarity, which can be challenging for rapidly evolving viruses. Pangenomes, offering a comprehensive representation of species’…
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…
Yutian Chen, Hongliang Lu, Xianglin Huang
Hyperspectral image (HSI) classification faces challenges in diverse scenarios due to spectral-spatial complexity and class imbalance. Existing methods lack generalizability. This paper presents a novel Graph-Convolutional Networks with Adaptive Region Ensembles (GCN-ARE) framework. It integrates graph spectral…
Yang Liu, Jason Huse, Kasthuri Kannan
Biomarker discovery for complex diseases, such as cancer, hinges on uncovering molecular signatures that capture intricate, interconnected relationships within biological data-a challenge that traditional statistical and machine learning methods often fail to meet due to the complexity of high-dimensional gene…
Raphael Mostov, Greyson Lewis, Gabriel Sturm, Wallace F. Marshall
This paper addresses the increasing need for comprehensive mathematical descriptions of cell organization by examining the algebraic structure of mitochondrial network dynamics. Mitochondria are cellular structures involved in metabolism that take the form of a network of membrane-based tubes that undergo continuous…
Aina Ferrà Marcús, Robert Jankowski, Meritxell Vila-Miñana, Carles Casacuberta + 1 more
Many complex networks, ranging from social to biological systems, exhibit structural patterns consistent with an underlying hyperbolic geometry. Revealing the dimensionality of this latent space can disentangle the structural complexity of communities, impact efficient network navigation, and fundamentally shape…
John Hood, Caterina De Bacco, Aaron Schein
Complex systems are often driven by higher-order interactions among multiple units, naturally represented as hypergraphs. Understanding dependency structures within these hypergraphs is crucial for understanding and predicting the behavior of complex systems but is made challenging by their combinatorial complexity and…
Alexandru Oarga, Matthew Hart, Andres M. Bran, Magdalena Lederbauer + 1 more
Knowledge graphs (KGs) are powerful tools for structured information modeling, increasingly recognized for their potential to enhance the factuality and reasoning capabilities of Large Language Models (LLMs). However, in scientific domains, KG representation is often constrained by the absence of ontologies capable of…
Zhong Li, Jiayang Shi, Matthijs van Leeuwen
Event logs are widely used to record the status of high-tech systems, making log anomaly detection important for monitoring those systems. Most existing log anomaly detection methods take a log event count matrix or log event sequences as input, exploiting quantitative and/or sequential relationships between log events…
Paweł Dłotko, Davide Gurnari, Mathis Hallier, Anna Jurek-Loughrey
At scale, computing the ClusterGraph, evaluating the metric distortion, and performing the iterative pruning can become computationally demanding. A detailed complexity analysis is provided in Supplementary_Appendix.pdf. Several strategies can keep the ClusterGraph tractable on large datasets. Exact…