24 papers · ranked by Valyu relevance
Sadegh Motallebi, Sadegh Aliakbary, Jafar Habibi
Real networks exhibit nontrivial topological features such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the…
Varsha Chauhan, Alexander Gutfraind, Ilya Safro
A network is a representation of a set of entities and the relationships between them. The network paradigm is often used to represent physical, biological, engineered and social systems [1]. Networks can help us better understand the structural and functional dynamics of these systems and formulate predictive models.…
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…
Khalid Bakhshaliyev, Mehmet Hadi Gunes, Gabriele Oliva
Comprehensive analysis that aims to understand the topology of real-world networks and the development of algorithms that replicate their characteristics has been significant research issues. Although the accuracy of newly developed network protocols or algorithms does not depend on the underlying topology, the…
Tommi Aho, Olli-Pekka Smolander, Jari Niemi, Olli Yli-Harja
Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model…
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…
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…
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 to identify principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modeling, in which wiring rules are…
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…
Dániel L. Barabási, Dániel Czégel
Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Long overlooked in this search has been the role of the innate: networks that connect based on identity-dependent compatibility rules. Inspired by the genetic…
Akmal Artikov, Aleksandr Dorodnykh, Yana Kashinskaya, Egor Samosvat
Background Several models for producing scale-free networks have been suggested; most of them are based on the preferential attachment approach. In this article, we suggest a new approach for generating scale-free networks with an alternative source of the power-law degree distribution. Methods The model derives from…
Jaejoon Choi, Doheon Lee
Network motifs are topological subgraph patterns that recur with statistical significance in a network. Network motifs have been widely utilized to represent important topological features for analyzing the functional properties of complex networks. While recent studies have shown the importance of network motifs…
Jin Xu, H Steven Wiley, Herbert M Sauro
Predictive models of signaling pathways have proven to be difficult to develop. The reasons for this include the uncertainty in the number of species, the complexity of species interactions, and the sparseness and uncertainty in experimental data. Traditional approaches to developing mechanistic models rely on…
Supun Perera, Michael 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…
Stuart Oldham, Alex Fornito, Gareth Ball
Generative network models (GNMs) have been proposed to identify the mechanisms/constraints that shape the organisation of the connectome. These models parameterise the formation of inter-regional connections using a trade-off between connection cost and topological complexity or biophysical similarity. Despite their…
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…
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…
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…
Nathan Mancheun Lui, Max D Li, Matthew Ford
Deep generative models for molecular graphs offer a new avenue for property optimization in drug discovery. Optimizing differentiable models that generate molecular graphs is certainly faster, cheaper, and much more accessible than traditional methods of chemical synthesis. Recent advances in generative modeling have…
Santiago Morandi, Oliver Loveday, Tim Renningholtz, Sergio Pablo-García + 6 more
Process optimization in heterogeneous catalysis relies on the control of competing reactions. The reaction mechanisms based on chemical knowledge can be evaluated via density functional theory unveiling experimental catalytic trends. However, this approach finds its limits when applied to complex reaction networks or…
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…
Philipp Renz, Dries Van Rompaey, Jörg Kurt Wegner, Sepp Hochreiter + 1 more
There has been a wave of generative models for molecules triggered by advances in the field of Deep Learning. These generative models are often used to optimize chemical compounds towards particular properties or a desired biological activity. The evaluation of generative models remains challenging and suggested…
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…
Zhimian Hao, Chonghuan Zhang, Alexei Lapkin
We propose a workflow for reduction in the time required for data generation during generation of statistical digital twins. This methodology is particularly relevant for real-world engineering problems when data generation is expensive. A prerequisite for building surrogates is sufficient input/output data, whereas…