25 papers · ranked by Valyu relevance
Erick Petersen, Jorge López, Natalia Kushik, Claude Poletti + 1 more
'Djamal Zeghlache'] Abstract: This paper presents the design and architecture of a network emulator whose links' parameters (such as delay and bandwidth) vary at different time instances. The emulator can thus be used in order to test and evaluate novel solutions for such networks, before their final deployment. To…
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…
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…
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…
SungJun Cho, Rukuang Huang, Chetan Gohil, Oiwi Parker Jones + 1 more
Functional brain network dynamics underlie fundamental aspects of human cognition and behaviour, including memory, ageing, and a range of clinical disorders. It has been shown that ongoing brain network dynamics can be reliably inferred at fast, sub-second timescales from electrophysiological data using unsupervised…
Chetan Gohil, Evan Roberts, Ryan Timms, Alex Skates + 8 more
Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these…
Bartosz Musznicki, Maciej Piechowiak, Piotr Zwierzykowski, Raffaele Bruno
'Raffaele Bruno'] Epidemics and pandemics dramatically affect mobility trends around the world, which we have witnessed recently and expect more of in the future. A global energy crisis is looming ahead on the horizon and will redefine the transportation and energy usage patterns, in particular in large cities and…
Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia + 1 more
'Alessandro Garavaglia' 'Nelly Litvak'] We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen…
Colin Graber, Yurii Vlasov, Alexander Schwing
We introduce a probabilistic model to discover the dynamic connectivity structure in a network of spiking cortical neurons. The model is evaluated on ground truth synthetic data and compared to alternative methods to ensure quality and quantification of model predictions. When applied to large-scale recordings of…
SungJun Cho, Rukuang Huang, Chetan Gohil, Oiwi Parker Jones + 1 more
Functional brain network dynamics underlie fundamental aspects of human cognition and behaviour, including memory, ageing, and a range of clinical disorders. It has been shown that ongoing brain network dynamics can be reliably inferred at fast, sub-second timescales from electrophysiological data using unsupervised…
Rumana Lakdawala, Joris Mulder, Roger Leenders
Many important social phenomena are characterized by repeated interactions among individuals over time such as email exchanges in an organization or face-to-face interactions in a classroom. To understand the underlying mechanisms of social interaction dynamics, statistical simulation techniques for network data at…
Tapan Chowdhury, Anindya Maitra, Anjali Agarwal, Arundhuti Sur + 3 more
Cell growth is an intricate biological phenomenon that is closely regulated by the interplay between various growth factors and transcription factors. Signaling pathways are the main mediators in this event, which provide the driving force for mitosis or sometimes meiosis. However, when malfunctions occur within the…
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…
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…
Chunyu Pan, Zhao Su, Changsheng Zhang, Xizhe Zhang
Real-world complex network systems often experience changes over time, and controlling their state has important applications in various fields. While external control signals can drive static networks to a desired state, dynamic networks have varying topologies that require changes to the driver nodes for maintaining…
Jianrun Shi, Leiyang Cui, Bo Gu, Bin Lyu + 2 more
'Leopoldo Angrisani'] Mobile traffic prediction enables the efficient utilization of network resources and enhances user experience. In this paper, we propose a state transition graph-based spatial-temporal attention network (STG-STAN) for cell-level mobile traffic prediction, which is designed to exploit the…
Leonardo Bellocchi, Vito Latora, Nikolas Geroliminis
Spatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. This is particularly true for urban transportation networks. The aim of this work is to find appropriate network measures that are able to detect critical zones for…
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…
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…
Authors not listed
The functionality of viral proteases, such as those from Dengue Virus (DENV) and SARS-CoV-2, is intrinsically linked to their conformational dynamics. While Molecular Dynamics (MD) is a powerful tool to study these motions, its computational cost limits large-scale screening. Here, we present a rapid analysis of the…
Yoshiharu Mukouyama, Kenya Tanaka, Shuji Nakanishi, Yoshihiro Nakato
The emergence of life on Earth has attracted intense attention but remains unclear. A key problem is that the question of how living organisms can exhibit self-organizing ability that leads to highly ordered structures and active free independent behavior remains unanswered. This work reveals, by computer simulation…
Sonny Young
Predicting molecular trajectories is a cornerstone of computational chemistry, with implications for drug discovery and molecular dynamics simulations. This study presents a comprehensive analysis of various machine learning models for the prediction of aspirin molecular trajectories, as captured in a dataset of 1500…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
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…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…