707 research outputs found
Analysis of weighted networks
The connections in many networks are not merely binary entities, either
present or not, but have associated weights that record their strengths
relative to one another. Recent studies of networks have, by and large, steered
clear of such weighted networks, which are often perceived as being harder to
analyze than their unweighted counterparts. Here we point out that weighted
networks can in many cases be analyzed using a simple mapping from a weighted
network to an unweighted multigraph, allowing us to apply standard techniques
for unweighted graphs to weighted ones as well. We give a number of examples of
the method, including an algorithm for detecting community structure in
weighted networks and a new and simple proof of the max-flow/min-cut theorem.Comment: 9 pages, 3 figure
Large-scale structure of time evolving citation networks
In this paper we examine a number of methods for probing and understanding
the large-scale structure of networks that evolve over time. We focus in
particular on citation networks, networks of references between documents such
as papers, patents, or court cases. We describe three different methods of
analysis, one based on an expectation-maximization algorithm, one based on
modularity optimization, and one based on eigenvector centrality. Using the
network of citations between opinions of the United States Supreme Court as an
example, we demonstrate how each of these methods can reveal significant
structural divisions in the network, and how, ultimately, the combination of
all three can help us develop a coherent overall picture of the network's
shape.Comment: 10 pages, 6 figures; journal names for 4 references fixe
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network
The application of deep learning to symbolic domains remains an active
research endeavour. Graph neural networks (GNN), consisting of trained neural
modules which can be arranged in different topologies at run time, are sound
alternatives to tackle relational problems which lend themselves to graph
representations. In this paper, we show that GNNs are capable of multitask
learning, which can be naturally enforced by training the model to refine a
single set of multidimensional embeddings and decode them
into multiple outputs by connecting MLPs at the end of the pipeline. We
demonstrate the multitask learning capability of the model in the relevant
relational problem of estimating network centrality measures, focusing
primarily on producing rankings based on these measures, i.e. is vertex
more central than vertex given centrality ?. We then show that a GNN
can be trained to develop a \emph{lingua franca} of vertex embeddings from
which all relevant information about any of the trained centrality measures can
be decoded. The proposed model achieves accuracy on a test dataset of
random instances with up to 128 vertices and is shown to generalise to larger
problem sizes. The model is also shown to obtain reasonable accuracy on a
dataset of real world instances with up to 4k vertices, vastly surpassing the
sizes of the largest instances with which the model was trained ().
Finally, we believe that our contributions attest to the potential of GNNs in
symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure
Cycle-centrality in complex networks
Networks are versatile representations of the interactions between entities
in complex systems. Cycles on such networks represent feedback processes which
play a central role in system dynamics. In this work, we introduce a measure of
the importance of any individual cycle, as the fraction of the total
information flow of the network passing through the cycle. This measure is
computationally cheap, numerically well-conditioned, induces a centrality
measure on arbitrary subgraphs and reduces to the eigenvector centrality on
vertices. We demonstrate that this measure accurately reflects the impact of
events on strategic ensembles of economic sectors, notably in the US economy.
As a second example, we show that in the protein-interaction network of the
plant Arabidopsis thaliana, a model based on cycle-centrality better accounts
for pathogen activity than the state-of-art one. This translates into
pathogen-targeted-proteins being concentrated in a small number of triads with
high cycle-centrality. Algorithms for computing the centrality of cycles and
subgraphs are available for download
The impact of partially missing communities~on the reliability of centrality measures
Network data is usually not error-free, and the absence of some nodes is a
very common type of measurement error. Studies have shown that the reliability
of centrality measures is severely affected by missing nodes. This paper
investigates the reliability of centrality measures when missing nodes are
likely to belong to the same community. We study the behavior of five commonly
used centrality measures in uniform and scale-free networks in various error
scenarios. We find that centrality measures are generally more reliable when
missing nodes are likely to belong to the same community than in cases in which
nodes are missing uniformly at random. In scale-free networks, the betweenness
centrality becomes, however, less reliable when missing nodes are more likely
to belong to the same community. Moreover, centrality measures in scale-free
networks are more reliable in networks with stronger community structure. In
contrast, we do not observe this effect for uniform networks. Our observations
suggest that the impact of missing nodes on the reliability of centrality
measures might not be as severe as the literature suggests
Constructing a logistics space: perspectives form the Gulf Cooperation Council
Development plans across the Gulf Cooperation Council emphasise logistical infrastructure as a driver of economic diversification. Investments in maritime ports, roads, rail, airports and logistics cities are transforming the economic geography of the region. This study aims to make visible this neglected aspect of the physical transformation of the Gulf Cooperation Council with a focus on the understudied maritime container ports in Oman and Qatar. Shifting the analysis to emergent maritime logistical infrastructure at a regional level gives insight into the uneven developments within the Gulf Cooperation Council’s integration project. Three key features emerge: (a) a large degree of duplication in maritime port infrastructure across Gulf Cooperation Council states; (b) a regional hierarchy among Gulf Cooperation Council states that are resource rich and those dependent on public–private partnerships and (c) increasing competition among internationally dominant port operators looking to gain access to the Gulf Cooperation Council maritime port market. These features both reflect and reinforce competitive tensions within the regional integration project
A Chinese Model for Labour in Europe?
Based on long-term fieldwork in multiple locations, our article questions the approach that posits a Chinese model of work and employment relations as increasingly exporting its form of labour management and dominating worldwide. It does so by focusing on Europe and discussing two labour regimes considered as typically Chinese: the Chinese fashion workshops in the Italian fashion industry, and the Foxconn electronics plants in the Czech Republic. Our findings bring new insights to bear on issues for which research is still thin on the ground and challenge the hypothesis of a \u2018Chinesisation\u2019 of work and employment practices in Chinese small firms and MNCs operating in Europe. We move the focus away from the simple analysis of firm management prevailing in the literature and suggest that, in order to understand the firm\u2019s behaviours, the role of the state, the unions, the migrant workers and the role of temporary work agencies should all be taken into consideration
Controlling centrality in complex networks
Spectral centrality measures allow to identify influential individuals in social groups, to rank Web pages by popularity, and even to determine the impact of scientific researches. The centrality score of a node within a network crucially depends on the entire pattern of connections, so that the usual approach is to compute node centralities once the network structure is assigned. We face here with the inverse problem, that is, we study how to modify the centrality scores of the nodes by acting on the structure of a given network. We show that there exist particular subsets of nodes, called controlling sets, which can assign any prescribed set of centrality values to all the nodes of a graph, by cooperatively tuning the weights of their out-going links. We found that many large networks from the real world have surprisingly small controlling sets, containing even less than 5 – 10% of the nodes
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