20 papers · ranked by Valyu relevance
Maria Letizia Bertotti, Giovanni Modanese
We generate correlated scale-free networks in the configuration model through a new rewiring algorithm that allows one to tune the Newman assortativity coefficient r and the average degree of the nearest neighbors K (in the range $-1\leqr\leq1$, $K\geq〈k〉$). At each attempted rewiring step, local variations $Δr$ and…
Ilias Rentzeperis, Cees van Leeuwen
Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are…
Raúl Luna, Jia Li, Roman Bauer, Cees van Leeuwen
Spontaneous retinal wave activity shaping the visual system is a complex neurodevelopmental phenomenon. Retinal ganglion cells are the hubs through which activity diverges throughout the visual system. We consider how these divergent hubs emerge, using an adaptively rewiring neural network model. Adaptive rewiring…
Jia Li, Ilias Rentzeperis, Cees van Leeuwen, Marieke Karlijn van Vugt
'Marieke Karlijn van Vugt'] Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular…
Magali Alexander Lopez-Chavira, Daniela Aguirre-Guerrero, Ricardo Marcelín-Jiménez, Luis Alberto Vásquez-Toledo + 1 more
'Ricardo Marcelín-Jiménez' 'Luis Alberto Vásquez-Toledo' 'Roberto Bernal-Jaquez'] We propose a distributed rewiring model which starts with a planar graph embedded into the Euclidean space and then behaves as a distributed system, where each node is provided with a set of dynamic links. The proposed rewiring evolves…
Jeff Alstott, Christine Klymko, Pamela B. Pyzza, Mary Radcliffe
Many real-world networks have high clustering among vertices: vertices that share neighbors are often also directly connected to each other. A network's clustering can be a useful indicator of its connectedness and community structure. Algorithms for generating networks with high clustering have been developed, but…
Cristina Chueca Del Cerro, Jennifer Badham, Yang Lou
Social networks typically have skewed degree distributions and relatively high clustering and assortativity coefficients. Some studies have explored the relationships between these properties, but have given limited attention to social networks and have found conflicting evidence. To expand our understanding of the…
Thibaut Morel-Journel, Pauline Ezanno, Elisabeta Vergu
The cattle tracing databases set up over the past decades in Europe have become major resources for representing demographic processes of livestock and assessing potential risk of infections spreading by trade. The herds registered in these databases are nodes of a network of commercial movements, which can be altered…
Filip Milisav, Vincent Bazinet, Richard F. Betzel, Bratislav Misic
Scientific discovery in connectomics relies on the use of network null models. To systematically evaluate the prominence of brain network features, empirical measures are compared against null statistics computed in randomized networks. Modern imaging and tracing technologies provide an increasingly rich repertoire of…
Francesco� Fabbri, Yanhao Wang, Francesco Bonchi, Carlos Fernández‐del Castillo + 1 more
'Carlos Fernández‐del Castillo' 'Michael Mathioudakis'] Recommender systems typically suggest to users content similar to what they consumed in the past. If a user happens to be exposed to strongly polarized content, she might subsequently receive recommendations which may steer her towards more and more radicalized…
Shane Mannion, Pádraig MacCarron, Akrati Saxena, Frank W. Takes
> In this paper we introduce a new, fast, degree-preserving rewiring algorithm for altering the assortativity of complex networks, which we call Fast total link (FTL) rewiring algorithm. Commonly used existing algorithms require a large number of iterations, in particular in the case of large dense networks. This can…
Shuo Zou, Bo Zhou, Qi Xuan
—Degree correlation is an important characteristic of networks, which is usually quantified by the assortativity coefficient. However, concerns arise about changing the assortativity coefficient of a network when networks suffer from adversarial attacks. In this paper, we analyze the factors that affect the…
Hugo Attali, Davide Buscaldi, Nathalie Pernelle
Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their effectiveness is often constrained by two critical challenges: oversquashing, where the excessive compression of information from distant nodes results in significant information loss, and oversmoothing, where repeated…
Shuo Zou, Bo Zhou, Qi Xuan
—Degree correlation is a crucial measure in networks, significantly impacting network topology and dynamical behavior. The degree sequence of a network is a significant characteristic, and altering network degree correlation through degreepreserving rewiring poses an interesting problem. In this paper, we define the…
Francesco Iorio, Andrea Gobbi, Thomas Cokelaer, Marti Bernardo Faura + 2 more
Networks are popular and powerful tools to describe and model biological processes. Many computational methods have been developed to infer biological networks from literature, high-throughput experiments, and combinations of both. Additionally, a wide range of tools has been developed to calibrate experimental data…
Michael W. Reimann, Daniela Egas-Santander
Neuronal connectivity has been characterized at various scales and with respect to various structural aspects. In models of connectivity, it has so far remained difficult to match all of them at once, in particular the higher-order structure appears to be elusive. Here we introduce a new type of graph model that…
Alexander Borst, Winfried Denk
Volume electron microscopy together with computer-based image analysis are yielding neural circuit diagrams of ever larger regions of the brain [1-10]. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs allowing to directly test…
Camilo J. Mininni, B. Silvano Zanutto
A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypothesis regarding the algorithms instantiated in the dynamic of a neural population, its firing statistics, and the underlying…
Authors not listed
Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…
Authors not listed
For applications in gas sensing, purification, and capture, we often wish to search a large set of metal-organic frameworks (MOFs) for the top-K in terms of their Henry coefficient of an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample…