528,727 research outputs found
Community detection in complex networks using Extremal Optimization
We propose a novel method to find the community structure in complex networks
based on an extremal optimization of the value of modularity. The method
outperforms the optimal modularity found by the existing algorithms in the
literature. We present the results of the algorithm for computer simulated and
real networks and compare them with other approaches. The efficiency and
accuracy of the method make it feasible to be used for the accurate
identification of community structure in large complex networks.Comment: 4 pages, 4 figure
Accuracy and Precision of Methods for Community Identification in Weighted Networks
Based on brief review of approaches for community identification and
measurement for sensitivity characterization, the accuracy and precision of
several approaches for detecting communities in weighted networks are
investigated. In weighted networks, the community structure should take both
links and link weights into account and the partition of networks should be
evaluated by weighted modularity . The results reveal that link weight has
important effects on communities especially in dense networks. Potts model and
Weighted Extremal Optimization (WEO) algorithm work well on weighted networks.
Then Potts model and WEO algorithms are used to detect communities in Rhesus
monkey network. The results gives nice understanding for real community
structure.Comment: 14 pages, 15 figure
Structural patterns in complex networks through spectral analysis
The study of some structural properties of networks is introduced from a graph spectral perspective. First, subgraph centrality of nodes is defined and used to classify essential proteins in a proteomic map. This index is then used to produce a method that allows the identification of superhomogeneous networks. At the same time this method classify non-homogeneous network into three universal classes of structure. We give examples of these classes from networks in different real-world scenarios. Finally, a communicability function is studied and showed as an alternative for defining communities in complex networks. Using this approach a community is unambiguously defined and an algorithm for its identification is proposed and exemplified in a real-world network
Community core detection in transportation networks
This work analyses methods for the identification and the stability under
perturbation of a territorial community structure with specific reference to
transportation networks. We considered networks of commuters for a city and an
insular region. In both cases, we have studied the distribution of commuters'
trips (i.e., home-to-work trips and viceversa). The identification and
stability of the communities' cores are linked to the land-use distribution
within the zone system, and therefore their proper definition may be useful to
transport planners.Comment: 8 pages, 13 figure
Comparing community structure identification
We compare recent approaches to community structure identification in terms
of sensitivity and computational cost. The recently proposed modularity measure
is revisited and the performance of the methods as applied to ad hoc networks
with known community structure, is compared. We find that the most accurate
methods tend to be more computationally expensive, and that both aspects need
to be considered when choosing a method for practical purposes. The work is
intended as an introduction as well as a proposal for a standard benchmark test
of community detection methods.Comment: 10 pages, 3 figures, 1 table. v2: condensed, updated version as
appears in JSTA
Robust Detection of Dynamic Community Structure in Networks
We describe techniques for the robust detection of community structure in
some classes of time-dependent networks. Specifically, we consider the use of
statistical null models for facilitating the principled identification of
structural modules in semi-decomposable systems. Null models play an important
role both in the optimization of quality functions such as modularity and in
the subsequent assessment of the statistical validity of identified community
structure. We examine the sensitivity of such methods to model parameters and
show how comparisons to null models can help identify system scales. By
considering a large number of optimizations, we quantify the variance of
network diagnostics over optimizations (`optimization variance') and over
randomizations of network structure (`randomization variance'). Because the
modularity quality function typically has a large number of nearly-degenerate
local optima for networks constructed using real data, we develop a method to
construct representative partitions that uses a null model to correct for
statistical noise in sets of partitions. To illustrate our results, we employ
ensembles of time-dependent networks extracted from both nonlinear oscillators
and empirical neuroscience data.Comment: 18 pages, 11 figure
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