Article
Computer Science, Information Systems
Yuede Ma, Matthias Dehmer, Urs-Martin Kuenzi, Shailesh Tripathi, Modjtaba Ghorbani, Jin Tao, Frank Emmert-Streib
Summary: This paper investigates the usefulness of topological graph measures and finds that many measures fail to solve problems effectively due to the selection of redundant and unfavorable graph invariants, as well as the lack of reflection in defining these measures. The paper extends the debate in the literature and quantitatively studies the usefulness of topological indices by assigning a feature vector to graphs that contains 'useful' properties represented by certain measures. The paper demonstrates examples and compares the usefulness using distance measures and an agglomerative clustering task.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Yuede Ma, Matthias Dehmer, Urs-Martin Kunzi, Abbe Mowshowitz, Shailesh Tripathi, Modjtaba Ghorbani, Frank Emmert-Streib
Summary: This paper compares two measures of graph symmetry based on the number and sizes of vertex orbits of the automorphism group, using a real valued distance measure to establish the limiting value of distances for several well-known classes of graphs.
INFORMATION SCIENCES
(2021)
Article
Biochemical Research Methods
Sridevi Maharaj, Taotao Qian, Zarin Ohiba, Wayne Hayes
Summary: The joint distribution of degree products and common neighbors has a greater impact on PPI edge connectivity than their individual distributions, leading to the introduction of two new models (CN and STICKY-CN). The inclusion of CN into STICKY-CN makes it the best overall fit for PPI networks as it is a good fit locally and globally.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Review
Automation & Control Systems
Resul Das, Mucahit Soylu
Summary: This comprehensive review provides an in-depth analysis of the role of graph theory and graph visualization in scientific studies. It explores different graph types, special graphs, and the challenges and advancements in graph visualization techniques. The review serves as a valuable resource for researchers to understand the principles and applications of graphs in diverse scientific disciplines.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xue Liu, Dan Sun, Wei Wei
Summary: This paper introduces a novel graph entropy definition to evaluate the smoothness of a data manifold and proposes a strategy to generate randomly perturbed training data while preserving both graph topology and graph entropy. Experimental results demonstrate the effectiveness of the method in improving semi-supervised node classification accuracy and enhancing the robustness of the training process for GCN.
PATTERN RECOGNITION
(2022)
Article
Neurosciences
Geraldine Rodriguez-Nieto, Oron Levin, Lize Hermans, Akila Weerasekera, Anca Croitor Sava, Astrid Haghebaert, Astrid Huybrechts, Koen Cuypers, Dante Mantini, Uwe Himmelreich, Stephan P. Swinnen
Summary: Aging is associated with structural and metabolic changes in the brain. Previous research has focused on individual brain regions, but the relationship among metabolites across the brain has been less studied. Using 1H-MRS, this study investigated the relationship among metabolite concentrations in different brain regions in young and older adults. The results showed age-related differences in metabolite concentrations and revealed associative patterns between metabolites across brain regions, which differed between age groups.
Article
Psychology, Mathematical
Martin Guest, Michele Zito, Johan Hulleman, Marco Bertamini
Summary: Observers have the ability to estimate the quantity of visual elements, which is influenced by specific visual properties. Graph theory-based measures can effectively model human performance in estimating the number of elements, with some measures sensitive to local clustering and others sensitive to density.
BEHAVIOR RESEARCH METHODS
(2022)
Article
Automation & Control Systems
Yasin Yazicioglu, Mudassir Shabbir, Waseem Abbas, Xenofon Koutsoukos
Summary: This paper studies the strong structural controllability of networks and compares two lower bounds on the dimension of the strong structurally controllable subspace (SSCS). It is found that the distance-based lower bound is usually better than the zero-forcing-based bound when the latter is smaller than the overall network state dimension. Furthermore, a novel bound based on combining these two approaches is introduced, which is always at least as good as, and sometimes strictly greater than, the maximum of the two original bounds.
Article
Automation & Control Systems
Shengling Shi, Xiaodong Cheng, Paul M. J. Van den Hof
Summary: This article investigates the identifiability conditions for single or multiple modules in a dynamic network and how to recover these modules by measuring the statistical properties of the signals. The conditions for identifying multiple modules are developed, and a method for allocating external excitation signals to achieve identifiability of specific subnetworks is introduced.
Article
Chemistry, Multidisciplinary
Drew A. Vecchio, Samuel H. Mahler, Mark D. Hammig, Nicholas A. Kotov
Summary: Materials with remarkable properties are structured as percolating nanoscale networks (PNNs), but their complex structures are difficult to describe using traditional methods and lack computational tools to capture patterns. The computational package StructuralGT generates GT descriptions of PNNs from micrographs, allowing rapid analysis of their structures. The GT parameters calculated can be correlated to specific material properties of PNNs for effective materials design.
Article
Biochemical Research Methods
Yelu Jiang, Lijun Quan, Kailong Li, Yan Li, Yiting Zhou, Tingfang Wu, Qiang Lyu
Summary: Effectively predicting protein-protein interactions after amino acid mutations is crucial for understanding protein function and designing drugs. This study proposes a deep graph convolution (DGC) network-based framework, DGCddG, which accurately predicts changes in protein-protein binding affinity after mutation. The model achieves good performance for both single and multi-point mutations, and shows promising results in predicting ACE2 changes in blind tests related to the SARS-CoV-2 virus.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Automation & Control Systems
Shengling Shi, Xiaodong Cheng, Paul M. J. Van den Hof
Summary: The identifiability of a single module in a network of transfer functions is determined by whether a particular transfer function can be uniquely distinguished within a network model set based on data. Generalized analysis results are developed for the situations of partial measurement and partial excitation. A novel network model structure is introduced to include excitation from unmeasured noise signals, leading to less conservative identifiability conditions.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Biochemistry & Molecular Biology
Hang Zhou, Weikun Wang, Jiayun Jin, Zengwei Zheng, Binbin Zhou
Summary: This paper presents a comparative study of various graph neural networks for protein-protein interaction prediction. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets.
Article
Psychology, Multidisciplinary
Patrick Smith, Steven C. Hayes
Summary: Relational models of cognition offer actionable models of generative behavior and guidance for computational analogs. However, the black box nature limits scientific and applied progress. This paper presents an attempt to model relational processes using logical derivation scripts and network graph visualizations, providing tools for exploring complex relational models.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Neurosciences
Ursula A. Tooley, Danielle S. Bassett, Allyson P. Mackey
Summary: The study found that the community structure of children's brains is similar to that of adults, but differences exist in transmodal areas. Children have more cortical territory in the limbic community, which is involved in emotion processing, than adults. Additionally, regions in association cortex interact more flexibly across communities, creating uncertainty.