9 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
Link Prediction via Community Detection in Bipartite Multi-Layer Graphs
International audienceThe growing number of multi-relational networks pose new challenges concerning the development of methods for solving classical graph problems in a multi-layer framework, such as link prediction. In this work, we combine an existing bipartite local models method with approaches for link prediction from communities to address the link prediction problem in multi-layer graphs. To this end, we extend existing community detection-based link prediction measures to the bi-partite multi-layer network setting. We obtain a new generic framework for link prediction in bipartite multi-layer graphs, which can integrate any community detection approach, is capable of handling an arbitrary number of networks, rather inexpensive (depending on the community detection technique), and able to automatically tune its parameters. We test our framework using two of the most common community detection methods, the Louvain algorithm and spectral partitioning, which can be easily applied to bipartite multi-layer graphs. We evaluate our approach on benchmark data sets for solving a common drug-target interaction prediction task in computational drug design and demonstrate experimentally that our approach is competitive with the state-of-the-art
Link Prediction via Community Detection in Bipartite Multi-Layer Graphs
International audienceThe growing number of multi-relational networks pose new challenges concerning the development of methods for solving classical graph problems in a multi-layer framework, such as link prediction. In this work, we combine an existing bipartite local models method with approaches for link prediction from communities to address the link prediction problem in multi-layer graphs. To this end, we extend existing community detection-based link prediction measures to the bipartite multi-layer network setting. We obtain a new generic framework for link prediction in bipartite multi-layer graphs, which can integrate any community detection approach, is capable of handling an arbitrary number of networks, rather inexpensive (depending on the community detection technique), and able to automatically tune its parameters. We test our framework using two of the most common community detection methods, the Louvain algorithm and spectral partitioning, which can be easily applied to bipartite multi-layer graphs. We evaluate our approach on benchmark data sets for solving a common drug-target interaction prediction task in computational drug design and demonstrate experimentally that our approach is competitive with the state-of-the-art