25 papers · ranked by Valyu relevance
Frank Emmert-Streib, Matthias Dehmer, Alex J. Cannon
The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We…
Hector Zenil, Narsis A. Kiani, Jesper Tegnér
Information-theoretic-based measures have been useful in quantifying network complexity. Here we briefly survey and contrast (algorithmic) information-theoretic methods which have been used to characterize graphs and networks. We illustrate the strengths and limitations of Shannon’s entropy, lossless compressibility…
Khaoula Ait Rai, Mustapha Machkour, Jilali Antari
Researchers have paid a lot of attention to complex networks in recent decades. Due to their rapid evolution, they turn into a major scientific and innovative field. Several studies on complex networks are carried out, and other subjects are evolving every day such as the challenge of detecting influential nodes. In…
Roberto Franzosi, Domenico Felice, Stefano Mancini, Marco Pettini
A central issue of the science of complex systems is the quantitative characterization of complexity. In the present work we address this issue by resorting to information geometry. Actually we propose a constructive way to associate to a - in principle any - network a differentiable object (a Riemannian manifold)…
Éverton Fernandes da Cunha, Luciano da Fontoura Costa
The concept of 'complexity' plays a central role in complex network science. Traditionally, this term has been taken to express heterogeneity of the node degrees of a therefore complex network. However, given that the degree distribution is not enough to provide an invertible representation of a given network…
Roberto C. Sotero, Lazaro M. Sanchez-Rodriguez, Narges Moradi
The complexity of brain activity has been observed at many spatial scales and there exists increasing evidence supporting its use in differentiating between mental states and disorders. Here we proposed a new measure of network (global) complexity that is constructed as the sum of the complexities of its nodes (i.e…
Merim Dzaferagic, Nicholas J. Kaminski, Neal McBride, Irene Macaluso + 1 more
'Nicola Marchetti'] Abstract The rapid evolution of network services demands new paradigms for studying and designing networks. We propose a framework to investigate the underlying mechanisms of wireless network functions. This framework isolates and analyses a network function as a complex system. We propose…
Keith Smith, Mark E. Bastin, Simon R. Cox, Maria C. Valdés Hernández + 3 more
The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have confounded attempts to model it constructively. This calls for…
Marc Wiedermann, Jonathan F. Donges, Jürgen Kurths, Reik V. Donner
Complex networks are usually characterized in terms of their topological, spatial, or informationtheoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it still remains a…
Domenico Felice, Stefano Mancini, Marco Pettini
We consider a Gaussian statistical model whose parameter space is given by the variances of random variables. Underlying this model we identify networks by interpreting random variables as sitting on vertices and their correlations as weighted edges among vertices. We then associate to the parameter space a statistical…
Daniela Egas Santander, Christoph Pokorny, András Ecker, Jānis Lazovskis + 5 more
'Jānis Lazovskis' 'Matteo Santoro' 'Jason P. Smith' 'Kathryn Hess' 'Ran Levi' 'Michael W. Reimann'] Title: Summary We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different…
Tuomo Mäki-Marttunen, Juha Kesseli, Matti Nykter, Derek Abbott
Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity…
Mayank Kejriwal, Ke Shen
In recent years, there has been a growing recognition that higher-order structures are important features in real-world networks. A particular class of structures that has gained prominence is known as a simplicial complex. Despite their application to complex processes such as social contagion and novel measures of…
Andrea Santoro, Vincenzo Nicosia
Multilayer networks preserve full information about the different interactions among the constituents of a complex system, and have recently proven quite useful in modelling transportation networks, social circles, and the human brain. A fundamental and still open problem is to assess if and when the multilayer…
Harrison B. Smith, Hyunju Kim, Sara I. Walker
Biochemical reactions underlie all living processes. Like many biological and technological systems, their complex web of interactions is difficult to fully capture and quantify with simple mathematical objects. Nonetheless, a huge volume of research has suggested many real-world biological and technological systems –…
Insuk Lee, Eiru Kim, Edward M. Marcotte
We find that the topologies of real world networks, such as those formed within human societies, by the Internet, or among cellular proteins, are dominated by the mode of the interactions considered among the individuals. Consequently, a major dichotomy in previously studied networks arises from modeling networks in…
Authors not listed
Active assembly of matter is a defining trait of living systems, enabling the creation of far-from-equilibrium materials essential for the functionality of life. This is achieved through energy-dissipative, multi-step processes facilitated by various biomolecular agents for chemical and mechanical assembly of matter.…
Rahul K Verma, Pramod Shinde, Ankit Mishra, Sarika Jalan
Pair-wise co-mutation networks of the mitochondrial genome have already provided ample evidences about the roles of genetic interactions in the manifestation of phenotype under altered environmental conditions. Here, we present a method to construct and analyze higher-order interactions, namely, 3-uniform hypergraphs…
Benjamin Ries, Richard J Gowers, James RB Eastwood, Irfan Alibay + 4 more
Alchemical free energy campaigns can be planned using graph theory by building up networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy…
T. Poisot, A. Cirtwill, D. Gravel, M.-J. Fortin + 1 more
There is a growing realization among community ecologists that interactions between species vary in space and time. Yet, our current numerical framework to analyze the structure of interactions, largely based on graph-theoretical approaches, is unsuited to this type of data. Since the variation of species interactions…
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…
Ginestra Bianconi, Christoph Rahmede
A large variety of interacting complex systems are characterized by interactions occurring between more than two nodes. These systems are described by simplicial complexes. Simplicial complexes are formed by simplices (nodes, links, triangles, tetrahedra etc.) that have a natural geometric interpretation. As such…
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Shyam Dwaraknath + 2 more
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are not well understood and products are unknown. Here we report new methods of CRN generation and analysis that overcome…
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Shyam Dwaraknath + 2 more
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are unintuitive, and products are unknown. Here we report new methods of CRN generation and analysis that overcome these…
Alexander Smith, Spencer Runde, Alex Chew, Atharva Kelkar + 3 more
Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex datasets that capture the 3D spatial positions, dynamics, and interactions of thousands…