25 papers · ranked by Valyu relevance
Zoran Levnajić
Understanding the processes behind the evolution of complex networks is a key objective in network science. An effective framework for tackling this challenge is network model selection, which involves finding the model from a set of candidates that best explains a given network. This book is a systematic review of…
Supun Perera, Michael G.H. Bell, Michiel C.J. Bliemer
Due to the increasingly complex and interconnected nature of global supply chain networks (SCNs), a recent strand of research has applied network science methods to model SCN growth and subsequently analyse various topological features, such as robustness. This paper provides: (1) a comprehensive review of the…
Danielle S. Bassett, Perry Zurn, Joshua I. Gold
From interacting cellular components to networks of neurons and neural systems, interconnected units comprise a fundamental organizing principle of the nervous system. Understanding how their patterns of connections and interactions give rise to the many functions of the nervous system is a primary goal of…
Alexander P. Clark, Mukti Chowkwale, Alexander Paap, Stephen Dang + 1 more
'Jeffrey J. Saucerman'] Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational…
Alexander P. Clark, Mukti Chowkwale, Alexander Paap, Stephen Dang + 1 more
Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools…
Mason A. Porter, Sam Howison
Many problems in industry — and in the social, natural, information, and medical sciences — involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. The study of networks is concerned explicitly with connectivity between…
Yongheng Zhang, Yuliang Lu, Guozheng Yang, Dongdong Hou + 5 more
'Boleslaw K. Szymanski' 'Jianxi Gao' 'Lu Zhong' 'Xueming Liu'] The Internet creates multidimensional and complex relationships in terms of the composition, application and mapping of social users. Most of the previous related research has focused on the single-layer topology of physical device networks but ignored the…
Miquel Ferriol-Galmés, Krzysztof Rusek, José Suárez‐Varela, Shihan Xiao + 3 more
'Shihan Xiao' 'Xiangle Cheng' 'Pere Barlet‐Ros' 'Albert Cabellos‐Aparicio'] Abstract—Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival…
Naomi A. Arnold, Raul J. Mondragón, Richard G. Clegg
Discriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of…
Chin Ying Liew, Jane Labadin, Woon Chee Kok, Monday Okpoto Eze
The graph-theoretic based studies employing bipartite network approach mostly focus on surveying the statistical properties of the structure and behavior of the network systems under the domain of complex network analysis. They aim to provide the big-picture-view insights of a networked system by looking into the…
Mustafa Ozen, Ali Abdi, Effat S. Emamian
Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding the effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network…
Omar Aloui, David Orden, N. Bel Hadj Ali, Landolf Rhode‐Barbarigos
Network equilibrium models represent a versatile tool for the analysis of interconnected objects and their relationships. They have been widely employed in both science and engineering to study the behavior of complex systems under various conditions, including external perturbations and damage. In this paper, network…
Daniel N Mohsenizadeh, Jianping Hua, Michael Bittner, Edward R Dougherty
'Edward R Dougherty'] Background Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive…
Richard F. Betzel, Danielle S. Bassett
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are…
Bin Wu, Sifu Luo, C. Steve Suh
— Understanding the mechanisms of propagation in complex networks is critical for various domains such as epidemiology, social media, communication networks, and multirobot systems. This paper provides a comprehensive review of propagation models in complex networks, ranging from traditional deterministic models to…
Luis Sordo Vieira, Paola Vera-Licona
Different objects exist to describe how a signal transduces in a given intracellular signaling network, such as elementary signaling modes, T-invariants, extreme pathway analysis, elementary modes and simple paths. For modeling frameworks such as Boolean networks, Petri nets and hypergraphs, these signal transduction…
Raima Carol Appaw, Matthew J Silk, Julie Rushmore, Kimberly VanderWaal + 2 more
Detecting patterns in animal social behaviour and movement is complicated by the diversity of ecological, evolutionary, environmental, and biological drivers of these behaviours such as migration, foraging, assortative mixing, socio-ecological factors, and human influence. Addressing these complexities requires a…
Alan Veliz-Cuba, Stephen Randal Voss, David Murrugarra
A primary challenge in building predictive models from temporal data is selecting the appropriate network and the regulatory functions that describe the data. Software packages are available for equation learning of continuous models, but not for discrete models. In this paper we introduce a method for building model…
Todd Hylton
A thermodynamically motivated neural network model is described that self-organizes to transport charge associated with internal and external potentials while in contact with a thermal reservoir. The model integrates techniques for rapid, large-scale, reversible, conservative equilibration of node states and slow…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Authors not listed
Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically evaluated on in-distribution (ID) data, i.e., data originating from the same distribution as the training data. However, the real-world applications of such…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Aleksandr Fedorov, Anna Perechodjuk, David Linke
Artificial neural networks (ANNs) are powerful tools for solving a wide range of tasks in fundamental and applied science. However, training and building reliable ANN models requires a lot of data which so far hinders their wider application in kinetic modelling where typically only small (experimental) datasets are…