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Related papers: Shortest-path percolation on scale-free networks

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We propose a new random pruning method (called "submodular sparsification (SS)") to reduce the cost of submodular maximization. The pruning is applied via a "submodularity graph" over the $n$ ground elements, where each directed edge is…

Machine Learning · Computer Science 2016-06-02 Tianyi Zhou , Hua Ouyang , Yi Chang , Jeff Bilmes , Carlos Guestrin

We introduce a simple one-parameter network growth algorithm which is able to reproduce a wide variety of realistic network structures but without having to invoke any global information about node degrees such as preferential-attachment…

Statistical Mechanics · Physics 2007-05-23 David M. D. Smith , Chiu Fan Lee , Neil F. Johnson

Transition systems (TS) and Petri nets (PN) are important models of computation ubiquitous in formal methods for modeling systems. An important problem is how to extract from a given TS a PN whose reachability graph is equivalent (with a…

Formal Languages and Automata Theory · Computer Science 2022-05-05 Viktor Teren , Jordi Cortadella , Tiziano Villa

Small-world networks are the focus of recent interest because they appear to circumvent many of the limitations of either random networks or regular lattices as frameworks for the study of interaction networks of complex systems. Here, we…

Statistical Mechanics · Physics 2012-03-08 Luis A. Nunes Amaral , Antonio Scala , Marc Barthelemy , H. Eugene Stanley

A Barab\'asi-Albert scale-free network is constructed whose nodes are the Poisson distributed random points within a unit square and links are the straight line connections among the nodes. The cost function, which is the total wiring…

Statistical Mechanics · Physics 2009-11-10 S. S. Manna , A. Kabakcioglu

The shortest path problem is among the most fundamental combinatorial optimization problems to answer reachability queries. It is hard to deter-mine which vertices or edges are visited during shortest path traversals. In this paper, we…

Social and Information Networks · Computer Science 2014-12-30 Waqas Nawaz , Kifayat Ullah Khan , Young-Koo Lee

Practical wireless networks are finite, and hence non-stationary with nodes typically non-homo-geneously deployed over the area. This leads to a location-dependent performance and to boundary effects which are both often neglected in…

Information Theory · Computer Science 2014-01-03 Ralph Tanbourgi , Holger Jäkel , Friedrich K. Jondral

Mini-batch sub-sampling in neural network training is unavoidable, due to growing data demands, memory-limited computational resources such as graphical processing units (GPUs), and the dynamics of on-line learning. In this study we…

Machine Learning · Statistics 2020-04-07 Dominic Kafka , Daniel Wilke

Percolation, the formation of a macroscopic connected component, is a key feature in the description of complex networks. The dynamical properties of a variety of systems can be understood in terms of percolation, including the robustness…

Statistical Mechanics · Physics 2013-06-24 Shane Squires , Katherine Sytwu , Diego Alcala , Thomas Antonsen , Edward Ott , Michelle Girvan

Using the finite-size scaling, we have investigated the percolation phase transitions of evolving random networks under a generalized Achlioptas process (GAP). During this GAP, the edge with minimum product of two connecting cluster sizes…

Statistical Mechanics · Physics 2012-01-13 Jingfang Fan , Maoxin Liu , Liangsheng Li , Xiaosong Chen

We analyse the so-called small-world network model (originally devised by Strogatz and Watts), treating it, among other things, as a case study of non-linear coupled difference or differential equations. We derive a system of evolution…

Statistical Mechanics · Physics 2016-08-31 Andreas Lochmann , Manfred Requardt

Scaling behavior of scale-free evolving networks arising in communications, citations, collaborations, etc. areas is studied. We derive universal scaling relations describing properties of such networks and indicate limits of their…

Condensed Matter · Physics 2009-10-31 S. N. Dorogovtsev , J. F. F. Mendes

We consider single-particle quantum transport on parametrized complex networks. Based on general arguments regarding the spectrum of the corresponding Hamiltonian, we derive bounds for a measure of the global transport efficiency defined by…

Quantum Physics · Physics 2015-09-18 Nikolaj Kulvelis , Maxim Dolgushev , Oliver Muelken

Recent work on the internet, social networks, and the power grid has addressed the resilience of these networks to either random or targeted deletion of network nodes. Such deletions include, for example, the failure of internet routers or…

Statistical Mechanics · Physics 2009-10-31 D. S. Callaway , M. E. J. Newman , S. H. Strogatz , D. J. Watts

We study a modified version of a model previously proposed by Jackson and Wolinsky to account for communicating information and allocating goods in socioeconomic networks. In the model, the utility function of each node is given by a…

Physics and Society · Physics 2009-11-13 Rui Carvalho , Giulia Iori

The capacity (or maximum flow) of an unicast network is known to be equal to the minimum s-t cut capacity due to the max-flow min-cut theorem. If the topology of a network (or link capacities) is dynamically changing or unknown, it is not…

Information Theory · Computer Science 2015-06-12 Yuki Fujii , Tadashi Wadayama

Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient paradigm for machine intelligence. However, the current SNN…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Wenjie Wei , Malu Zhang , Zijian Zhou , Ammar Belatreche , Yimeng Shan , Yu Liang , Honglin Cao , Jieyuan Zhang , Yang Yang

Graphs are commonly used to model various complex systems, including social networks, power grids, transportation networks, and biological systems. In many applications, the connectivity of these networks can be expressed through the Mean…

Optimization and Control · Mathematics 2025-09-03 Christian P. C. Franssen , Alessandro Zocca , Bernd F. Heidergott

Slimmable Neural Networks (S-Net) is a novel network which enabled to select one of the predefined proportions of channels (sub-network) dynamically depending on the current computational resource availability. The accuracy of each…

Computer Vision and Pattern Recognition · Computer Science 2022-12-08 Hideaki Kuratsu , Atsuyoshi Nakamura

Recently, a race towards the simplification of deep networks has begun, showing that it is effectively possible to reduce the size of these models with minimal or no performance loss. However, there is a general lack in understanding why…

Machine Learning · Computer Science 2022-12-29 Enzo Tartaglione , Andrea Bragagnolo , Marco Grangetto
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