634 research outputs found
Community detection in multiplex networks using locally adaptive random walks
Multiplex networks, a special type of multilayer networks, are increasingly
applied in many domains ranging from social media analytics to biology. A
common task in these applications concerns the detection of community
structures. Many existing algorithms for community detection in multiplexes
attempt to detect communities which are shared by all layers. In this article
we propose a community detection algorithm, LART (Locally Adaptive Random
Transitions), for the detection of communities that are shared by either some
or all the layers in the multiplex. The algorithm is based on a random walk on
the multiplex, and the transition probabilities defining the random walk are
allowed to depend on the local topological similarity between layers at any
given node so as to facilitate the exploration of communities across layers.
Based on this random walk, a node dissimilarity measure is derived and nodes
are clustered based on this distance in a hierarchical fashion. We present
experimental results using networks simulated under various scenarios to
showcase the performance of LART in comparison to related community detection
algorithms
Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks
Multiplex networks have become increasingly more prevalent in many fields,
and have emerged as a powerful tool for modeling the complexity of real
networks. There is a critical need for developing inference models for
multiplex networks that can take into account potential dependencies across
different layers, particularly when the aim is community detection. We add to a
limited literature by proposing a novel and efficient Bayesian model for
community detection in multiplex networks. A key feature of our approach is the
ability to model varying communities at different network layers. In contrast,
many existing models assume the same communities for all layers. Moreover, our
model automatically picks up the necessary number of communities at each layer
(as validated by real data examples). This is appealing, since deciding the
number of communities is a challenging aspect of community detection, and
especially so in the multiplex setting, if one allows the communities to change
across layers. Borrowing ideas from hierarchical Bayesian modeling, we use a
hierarchical Dirichlet prior to model community labels across layers, allowing
dependency in their structure. Given the community labels, a stochastic block
model (SBM) is assumed for each layer. We develop an efficient slice sampler
for sampling the posterior distribution of the community labels as well as the
link probabilities between communities. In doing so, we address some unique
challenges posed by coupling the complex likelihood of SBM with the
hierarchical nature of the prior on the labels. An extensive empirical
validation is performed on simulated and real data, demonstrating the superior
performance of the model over single-layer alternatives, as well as the ability
to uncover interesting structures in real networks
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
Detecting community structure in social networks is a fundamental problem
empowering us to identify groups of actors with similar interests. There have
been extensive works focusing on finding communities in static networks,
however, in reality, due to dynamic nature of social networks, they are
evolving continuously. Ignoring the dynamic aspect of social networks, neither
allows us to capture evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
Revisiting Resolution and Inter-Layer Coupling Factors in Modularity for Multilayer Networks
Modularity for multilayer networks, also called multislice modularity, is
parametric to a resolution factor and an inter-layer coupling factor. The
former is useful to express layer-specific relevance and the latter quantifies
the strength of node linkage across the layers of a network. However, such
parameters can be set arbitrarily, thus discarding any structure information at
graph or community level. Other issues are related to the inability of properly
modeling order relations over the layers, which is required for dynamic
networks.
In this paper we propose a new definition of modularity for multilayer
networks that aims to overcome major issues of existing multislice modularity.
We revise the role and semantics of the layer-specific resolution and
inter-layer coupling terms, and define parameter-free unsupervised approaches
for their computation, by using information from the within-layer and
inter-layer structures of the communities. Moreover, our formulation of
multilayer modularity is general enough to account for an available ordering of
the layers and relating constraints on layer coupling. Experimental evaluation
was conducted using three state-of-the-art methods for multilayer community
detection and nine real-world multilayer networks. Results have shown the
significance of our modularity, disclosing the effects of different
combinations of the resolution and inter-layer coupling functions. This work
can pave the way for the development of new optimization methods for
discovering community structures in multilayer networks.Comment: Accepted at the IEEE/ACM Conf. on Advances in Social Network Analysis
and Mining (ASONAM 2017
Fragmenting networks by targeting collective influencers at a mesoscopic level
A practical approach to protecting networks against epidemic processes such
as spreading of infectious diseases, malware, and harmful viral information is
to remove some influential nodes beforehand to fragment the network into small
components. Because determining the optimal order to remove nodes is a
computationally hard problem, various approximate algorithms have been proposed
to efficiently fragment networks by sequential node removal. Morone and Makse
proposed an algorithm employing the non-backtracking matrix of given networks,
which outperforms various existing algorithms. In fact, many empirical networks
have community structure, compromising the assumption of local tree-like
structure on which the original algorithm is based. We develop an immunization
algorithm by synergistically combining the Morone-Makse algorithm and coarse
graining of the network in which we regard a community as a supernode. In this
way, we aim to identify nodes that connect different communities at a
reasonable computational cost. The proposed algorithm works more efficiently
than the Morone-Makse and other algorithms on networks with community
structure.Comment: 5 figures, 3 tables, and SI include
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