1,112 research outputs found
Module identification in bipartite and directed networks
Modularity is one of the most prominent properties of real-world complex
networks. Here, we address the issue of module identification in two important
classes of networks: bipartite networks and directed unipartite networks. Nodes
in bipartite networks are divided into two non-overlapping sets, and the links
must have one end node from each set. Directed unipartite networks only have
one type of nodes, but links have an origin and an end. We show that directed
unipartite networks can be conviniently represented as bipartite networks for
module identification purposes. We report a novel approach especially suited
for module detection in bipartite networks, and define a set of random networks
that enable us to validate the new approach
Social encounter networks : collective properties and disease transmission
A fundamental challenge of modern infectious disease epidemiology is to quantify the networks of social and physical contacts through which transmission can occur. Understanding the collective properties of these interactions is critical for both accurate prediction of the spread of infection and determining optimal control measures. However, even the basic properties of such networks are poorly quantified, forcing predictions to be made based on strong assumptions concerning network structure. Here, we report on the results of a large-scale survey of social encounters mainly conducted in Great Britain. First, we characterize the distribution of contacts, which possesses a lognormal body and a power-law tail with an exponent of −2.45; we provide a plausible mechanistic model that captures this form. Analysis of the high level of local clustering of contacts reveals additional structure within the network, implying that social contacts are degree assortative. Finally, we describe the epidemiological implications of this local network structure: these contradict the usual predictions from networks with heavy-tailed degree distributions and contain public-health messages about control. Our findings help us to determine the types of realistic network structure that should be assumed in future population level studies of infection transmission, leading to better interpretations of epidemiological data and more appropriate policy decisions
Community Detection as an Inference Problem
We express community detection as an inference problem of determining the
most likely arrangement of communities. We then apply belief propagation and
mean-field theory to this problem, and show that this leads to fast, accurate
algorithms for community detection.Comment: 4 pages, 2 figure
Treaty Abuse in the Post-BEPS World: Analysis of the Policy Shift and Impact of the Principal Purpose Test for MNE Groups
In this article, the author discusses treaty abuse in the Post-BEPS world and focuses on the practical impact of the Principal Purpose Test (PPT rule) for MNE groups. The article analyses in particular the meaning of substance under the PPT rule and the consequences of denial of treaty benefits
Evaluating Local Community Methods in Networks
We present a new benchmarking procedure that is unambiguous and specific to
local community-finding methods, allowing one to compare the accuracy of
various methods. We apply this to new and existing algorithms. A simple class
of synthetic benchmark networks is also developed, capable of testing
properties specific to these local methods.Comment: 8 pages, 9 figures, code included with sourc
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