1,119 research outputs found
Resolution limit in community detection
Detecting community structure is fundamental to clarify the link between
structure and function in complex networks and is used for practical
applications in many disciplines. A successful method relies on the
optimization of a quantity called modularity [Newman and Girvan, Phys. Rev. E
69, 026113 (2004)], which is a quality index of a partition of a network into
communities. We find that modularity optimization may fail to identify modules
smaller than a scale which depends on the total number L of links of the
network and on the degree of interconnectedness of the modules, even in cases
where modules are unambiguously defined. The probability that a module conceals
well-defined substructures is the highest if the number of links internal to
the module is of the order of \sqrt{2L} or smaller. We discuss the practical
consequences of this result by analyzing partitions obtained through modularity
optimization in artificial and real networks.Comment: 8 pages, 3 figures. Clarification of definition of community in
Section II + minor revision
BJET Editorial November 2016
Greetings to the BJET community from the new editorial team. We took over in July 2016 and are excited about the opportunity to lead and shape this esteemed journal. In this editorial we outline our policies and vision for the future and report on our first few months in post
Evidential Communities for Complex Networks
Community detection is of great importance for understand-ing graph structure
in social networks. The communities in real-world networks are often
overlapped, i.e. some nodes may be a member of multiple clusters. How to
uncover the overlapping communities/clusters in a complex network is a general
problem in data mining of network data sets. In this paper, a novel algorithm
to identify overlapping communi-ties in complex networks by a combination of an
evidential modularity function, a spectral mapping method and evidential
c-means clustering is devised. Experimental results indicate that this
detection approach can take advantage of the theory of belief functions, and
preforms good both at detecting community structure and determining the
appropri-ate number of clusters. Moreover, the credal partition obtained by the
proposed method could give us a deeper insight into the graph structure
BJET Editorial for the 50th Anniversary Volume in 2019: Looking back, reaching forward
The Editors are thrilled to introduce the 50th Anniversary volume of the British Journal of Educational Technology (BJET). This momentous milestone has spurred us to share with the readership our pride and sense of responsibility for editing one of the top journals in the field
A Simple Model of Epidemics with Pathogen Mutation
We study how the interplay between the memory immune response and pathogen
mutation affects epidemic dynamics in two related models. The first explicitly
models pathogen mutation and individual memory immune responses, with contacted
individuals becoming infected only if they are exposed to strains that are
significantly different from other strains in their memory repertoire. The
second model is a reduction of the first to a system of difference equations.
In this case, individuals spend a fixed amount of time in a generalized immune
class. In both models, we observe four fundamentally different types of
behavior, depending on parameters: (1) pathogen extinction due to lack of
contact between individuals, (2) endemic infection (3) periodic epidemic
outbreaks, and (4) one or more outbreaks followed by extinction of the epidemic
due to extremely low minima in the oscillations. We analyze both models to
determine the location of each transition. Our main result is that pathogens in
highly connected populations must mutate rapidly in order to remain viable.Comment: 9 pages, 11 figure
Distributed Community Detection in Dynamic Graphs
Inspired by the increasing interest in self-organizing social opportunistic
networks, we investigate the problem of distributed detection of unknown
communities in dynamic random graphs. As a formal framework, we consider the
dynamic version of the well-studied \emph{Planted Bisection Model}
\sdG(n,p,q) where the node set of the network is partitioned into two
unknown communities and, at every time step, each possible edge is
active with probability if both nodes belong to the same community, while
it is active with probability (with ) otherwise. We also consider a
time-Markovian generalization of this model.
We propose a distributed protocol based on the popular \emph{Label
Propagation Algorithm} and prove that, when the ratio is larger than
(for an arbitrarily small constant ), the protocol finds the right
"planted" partition in time even when the snapshots of the dynamic
graph are sparse and disconnected (i.e. in the case ).Comment: Version I
Stochastic blockmodels with growing number of classes
We present asymptotic and finite-sample results on the use of stochastic
blockmodels for the analysis of network data. We show that the fraction of
misclassified network nodes converges in probability to zero under maximum
likelihood fitting when the number of classes is allowed to grow as the root of
the network size and the average network degree grows at least
poly-logarithmically in this size. We also establish finite-sample confidence
bounds on maximum-likelihood blockmodel parameter estimates from data
comprising independent Bernoulli random variates; these results hold uniformly
over class assignment. We provide simulations verifying the conditions
sufficient for our results, and conclude by fitting a logit parameterization of
a stochastic blockmodel with covariates to a network data example comprising a
collection of Facebook profiles, resulting in block estimates that reveal
residual structure.Comment: 12 pages, 3 figures; revised versio
A New Comparative Definition of Community and Corresponding Identifying Algorithm
In this paper, a new comparative definition for community in networks is
proposed and the corresponding detecting algorithm is given. A community is
defined as a set of nodes, which satisfy that each node's degree inside the
community should not be smaller than the node's degree toward any other
community. In the algorithm, the attractive force of a community to a node is
defined as the connections between them. Then employing attractive force based
self-organizing process, without any extra parameter, the best communities can
be detected. Several artificial and real-world networks, including Zachary
Karate club network and College football network are analyzed. The algorithm
works well in detecting communities and it also gives a nice description for
network division and group formation.Comment: 11 pages, 4 fihure
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Classifier combination methods need to make best use of the outputs of
multiple, imperfect classifiers to enable higher accuracy classifications. In
many situations, such as when human decisions need to be combined, the base
decisions can vary enormously in reliability. A Bayesian approach to such
uncertain combination allows us to infer the differences in performance between
individuals and to incorporate any available prior knowledge about their
abilities when training data is sparse. In this paper we explore Bayesian
classifier combination, using the computationally efficient framework of
variational Bayesian inference. We apply the approach to real data from a large
citizen science project, Galaxy Zoo Supernovae, and show that our method far
outperforms other established approaches to imperfect decision combination. We
go on to analyse the putative community structure of the decision makers, based
on their inferred decision making strategies, and show that natural groupings
are formed. Finally we present a dynamic Bayesian classifier combination
approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure
Semi-Supervised Overlapping Community Finding based on Label Propagation with Pairwise Constraints
Algorithms for detecting communities in complex networks are generally
unsupervised, relying solely on the structure of the network. However, these
methods can often fail to uncover meaningful groupings that reflect the
underlying communities in the data, particularly when those structures are
highly overlapping. One way to improve the usefulness of these algorithms is by
incorporating additional background information, which can be used as a source
of constraints to direct the community detection process. In this work, we
explore the potential of semi-supervised strategies to improve algorithms for
finding overlapping communities in networks. Specifically, we propose a new
method, based on label propagation, for finding communities using a limited
number of pairwise constraints. Evaluations on synthetic and real-world
datasets demonstrate the potential of this approach for uncovering meaningful
community structures in cases where each node can potentially belong to more
than one community.Comment: Fix table
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