26 papers · ranked by Valyu relevance
Monzilur Rahman, Ben D. B. Willmore, Andrew J. King, Nicol S. Harper
Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets.…
Giselle Zeno, Timothy La Fond, Jennifer Neville
Motifs, which have been established as building blocks for network structure, move beyond pair-wise connections to capture longerrange correlations in connections and activity. In spite of this, there are few generative graph models that consider higher-order network structures and even fewer that focus on using motifs…
Jianguo Ding, Pascal Bouvry
With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from system design, hardware, software, protocols, structures, integration, evolution, application, even to business…
Zhongyang Wang, Junchang Xin, Qi Chen, Zhiqiong Wang + 4 more
'Oliver Faust' 'Li Su' 'U Rajendra Acharya'] As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain’s connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and…
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…
Marion Buffard, Aurélien Desoeuvres, Aurélien Naldi, Clément Requilé + 2 more
We introduce LNetReduce, a tool that simplifies linear dynamic networks. Dynamic networks are represented as digraphs labeled by integer timescale orders. Such models describe deterministic or stochastic monomolecular chemical reaction networks, but also random walks on weighted protein-protein interaction networks…
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…
Ravi Goyal, Victor De Gruttola
We present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a…
Vincent Miele, Catherine Matias
In ecology, recent technological advances and long-term data studies now provide longitudinal interaction data (e.g. between individuals or species). Most often, time is the parameter along which interactions evolve but any other one-dimensional gradient (temperature, altitude, depth, humidity, etc.) can be considered.…
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…
Faraz Zaidi, Chris Muelder, Arnaud Sallaberry
| Title: | Analysis and Visualization of Dynamic Networks | | --- | --- | | Name: | Faraz Zaidi1 , Chris Muelder2 , Arnaud Sallaberry3 | | Affil./Addr. 1: | College of Computing and Information Sciences, Karachi Institute | | | of Economics and Technology (KIET), Karachi, Pakistan | | | faraz@pafkiet.edu.pk | |…
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…
Mohsen Bahrami, Paul J. Laurienti, Heather M. Shappell, Dale Dagenbach + 1 more
The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical…
Ankit N. Khambhati, Ann E. Sizemore, Richard F. Betzel, Danielle S. Bassett
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its…
Stefano Anzellotti, Dorit Kliemann, Nir Jacoby, Rebecca Saxe
Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift…
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…
Yuan Zhao, Shifeng Liu
The IT system of manufacturing enterprises usually has many problems, such as complex industrial software, different development languages, diverse communication protocols, and complex operation environment. Cloud service bus (CSB) technology based on service model encapsulates various applications existing in…
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…
Oriol Lordan, Jose M. Sallan, Akbar Ali
Most complex network analyses of transportation systems use simplified static representations obtained from existing connections in a time horizon. In static representations, travel times, waiting times and compatibility of schedules are neglected, thus losing relevant information. To obtain a more accurate description…
Mark Müller-Linow, Claus C. Hilgetag, Marc-Thorsten Hütt, Olaf Sporns
'Olaf Sporns'] This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for…
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…
Matthew Bailey, Mark Wilson
One of the critical tools of persistent homology is the persistence diagram. We demonstrate the applicability of a persistence diagram showing the existence of topological features (here rings in a 2D network) generated over time instead of space as a tool to analyse trajectories of biological networks. We show how the…
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…