250,701 research outputs found
An ensemble approach to the analysis of weighted networks
We present a new approach to the calculation of measures in weighted
networks, based on the translation of a weighted network into an ensemble of
edges. This leads to a straightforward generalization of any measure defined on
unweighted networks, such as the average degree of the nearest neighbours, the
clustering coefficient, the `betweenness', the distance between two nodes and
the diameter of a network. All these measures are well established for
unweighted networks but have hitherto proven difficult to define for weighted
networks. Further to introducing this approach we demonstrate its advantages by
applying the clustering coefficient constructed in this way to two real-world
weighted networks.Comment: 4 pages 3 figure
Efficient computation of the Weighted Clustering Coefficient
The clustering coefficient of an unweighted network has been extensively used to quantify how tightly connected is the neighbor around a node and it has been widely adopted for assessing the quality of nodes in a social network. The computation of the clustering coefficient is challenging since it requires to count the number of triangles in the graph. Several recent works proposed efficient sampling, streaming and MapReduce algorithms that allow to overcome this computational bottleneck. As a matter of fact, the intensity of the interaction between nodes, that is usually represented with weights on the edges of the graph, is also an important measure of the statistical cohesiveness of a network. Recently various notions of weighted clustering coefficient have been proposed but all those techniques are hard to implement on large-scale graphs. In this work we show how standard sampling techniques can be used to obtain efficient estimators for the most commonly used measures of weighted clustering coefficient. Furthermore we also propose a novel graph-theoretic notion of clustering coefficient in weighted networks. © 2016, Copyright © Taylor & Francis Group, LL
Applying weighted network measures to microarray distance matrices
In recent work we presented a new approach to the analysis of weighted
networks, by providing a straightforward generalization of any network measure
defined on unweighted networks. This approach is based on the translation of a
weighted network into an ensemble of edges, and is particularly suited to the
analysis of fully connected weighted networks. Here we apply our method to
several such networks including distance matrices, and show that the clustering
coefficient, constructed by using the ensemble approach, provides meaningful
insights into the systems studied. In the particular case of two data sets from
microarray experiments the clustering coefficient identifies a number of
biologically significant genes, outperforming existing identification
approaches.Comment: Accepted for publication in J. Phys.
A unified approach to mapping and clustering of bibliometric networks
In the analysis of bibliometric networks, researchers often use mapping and
clustering techniques in a combined fashion. Typically, however, mapping and
clustering techniques that are used together rely on very different ideas and
assumptions. We propose a unified approach to mapping and clustering of
bibliometric networks. We show that the VOS mapping technique and a weighted
and parameterized variant of modularity-based clustering can both be derived
from the same underlying principle. We illustrate our proposed approach by
producing a combined mapping and clustering of the most frequently cited
publications that appeared in the field of information science in the period
1999-2008
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