22 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…
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…
José Suárez‐Varela, Paul Almasan, Miquel Ferriol-Galmés, Krzysztof Rusek + 7 more
'Krzysztof Rusek' 'Fabien Geyer' 'Xiangle Cheng' 'Xiang Shi' 'Shihan Xiao' 'Franco Scarselli' 'Albert Cabellos‐Aparicio' 'Pere Barlet‐Ros'] © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing…
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…
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…
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…
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…
Jiaqi Wen, Bogdan Gabryś, Katarzyna Musiał
—This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality. We propose a new framework to conceptually…
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…
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…
Wenjun Zhang, Xiangna Chen, Weibing Deng, Alexandre G. Evsukoff + 1 more
'Yilun Shang'] The L-space and P-space are two essential representations for studying complex networks that contain different clusters. Existing network models can successfully generate networks in L-space, but generating networks in P-space poses significant challenges. In this study, we present an empirical analysis…
Johanna Senk, Birgit Kriener, Mikael Djurfeldt, Nicole Voges + 7 more
'Han-Jia Jiang' 'Lisa Schüttler' 'Gabriele Gramelsberger' 'Markus Diesmann' 'Hans E. Plesser' 'Sacha J. van Albada' 'Roberto Toro'] Sustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about…
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…
Malgorzata Kardynska, Daria Kogut, Marcin Pacholczyk, Jaroslaw Smieja
'Jaroslaw Smieja'] Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract…
Kishore Hari, William Duncan, Mohammed Adil Ibrahim, Mohit Kumar Jolly + 2 more
Mathematical modeling of the emergent dynamics of gene regulatory networks (GRN) faces a double challenge of (a) dependence of model dynamics on parameters, and (b) lack of reliable experimentally determined parameters. In this paper we compare two complementary approaches for describing GRN dynamics across unknown…
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…
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…