Results 11 to 20 of about 605,479 (264)
Centrality measures in networks
Social Choice and Welfare, 2023 We show that prominent centrality measures in network analysis are all based on additively separable and linear treatments of statistics that capture a node's position in the network. This enables us to provide a taxonomy of centrality measures that distills them to varying on two dimensions: (i) which information they make use of about nodes ...
Francis Bloch +2 more
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Network measures of mixing [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019 Transport and mixing processes in fluid flows can be studied directly from Lagrangian trajectory data, such as those obtained from particle tracking experiments. Recent work in this context highlights the application of graph-based approaches, where trajectories serve as nodes and some similarity or distance measure between them is employed to build a (
Ralf Banisch +2 more
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On measuring network robustness for weighted networks
Knowledge and Information Systems, 2022 Network robustness measures how well network structure is strong and healthy when it is under attack, such as vertices joining and leaving. It has been widely used in many applications, such as information diffusion, disease transmission, and network security.
Jianbing Zheng 0001 +5 more
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Invariance measures for neural networks
Applied Soft Computing, 2023 Invariances in neural networks are useful and necessary for many tasks. However, the representation of the invariance of most neural network models has not been characterized. We propose measures to quantify the invariance of neural networks in terms of their internal representation.
Facundo Manuel Quiroga +3 more
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Verifiable network-performance measurements [PDF]
Proceedings of the 6th International COnference, 2010 14 ...
Katerina J. Argyraki +2 more
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Measuring the mixing time of a network [PDF]
2015 IEEE Conference on Computer Communications (INFOCOM), 2015 Mixing time is a global property of a network that indicates how fast a random walk gains independence from its starting point. Mixing time is an essential parameter for many distributed algorithms, but especially those based on gossip. We design, implement, and evaluate a distributed protocol to measure mixing time.
Xenofon Foukas +2 more
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Measuring Network Robustness by Average Network Flow [PDF]
IEEE Transactions on Network Science and Engineering, 2022 Infrastructure networks such as the Internet backbone and power grids are essential for our everyday lives. With the prevalence of cyber-attacks on them, measuring their robustness has become an important issue. To date, many robustness metrics have been proposed.
Weisheng Si +3 more
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Measuring the hierarchy of feedforward networks [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2011 In this paper we explore the concept of hierarchy as a quantifiable descriptor of ordered structures, departing from the definition of three conditions to be satisfied for a hierarchical structure: order, predictability, and pyramidal structure. According to these principles, we define a hierarchical index taking concepts from graph and information ...
Corominas Murtra, Bernat +3 more
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Benchmarking Measures of Network Influence [PDF]
Scientific Reports, 2016 AbstractIdentifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness ...
Bramson, Aaron, Vandermarliere, Benjamin
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Transport of measures on networks
Networks & Heterogeneous Media, 2017 In this paper we formulate a theory of measure-valued linear transport equations on networks. The building block of our approach is the initial/boundary-value problem for the measure-valued linear transport equation on a bounded interval, which is the prototype of an arc of the network. For this problem we give an explicit representation formula of the
CAMILLI, FABIO +2 more
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