122 research outputs found
Dynamic degree-corrected blockmodels for social networks: A nonparametric approach
A nonparametric approach to the modelling of social networks using degree-corrected stochastic blockmodels is proposed. The model for static network consists of a stochastic blockmodel using a probit regression formulation, and popularity parameters are incorporated to account for degree heterogeneity. We specify a Dirichlet process prior to detect community structure as well as to induce clustering in the popularity parameters. This approach is flexible yet parsimonious as it allows the appropriate number of communities and popularity clusters to be determined automatically by the data. We further discuss and implement extensions of the static model to dynamic networks. In a Bayesian framework, we perform posterior inference through MCMC algorithms. The models are illustrated using several real-world benchmark social networks
On the relationship between Gaussian stochastic blockmodels and label propagation algorithms
The problem of community detection receives great attention in recent years.
Many methods have been proposed to discover communities in networks. In this
paper, we propose a Gaussian stochastic blockmodel that uses Gaussian
distributions to fit weight of edges in networks for non-overlapping community
detection. The maximum likelihood estimation of this model has the same
objective function as general label propagation with node preference. The node
preference of a specific vertex turns out to be a value proportional to the
intra-community eigenvector centrality (the corresponding entry in principal
eigenvector of the adjacency matrix of the subgraph inside that vertex's
community) under maximum likelihood estimation. Additionally, the maximum
likelihood estimation of a constrained version of our model is highly related
to another extension of label propagation algorithm, namely, the label
propagation algorithm under constraint. Experiments show that the proposed
Gaussian stochastic blockmodel performs well on various benchmark networks.Comment: 22 pages, 17 figure
Deep Learning in Social Networks for Overlappering Community Detection
The collection of nodes is termed as community in any network system that are tightly associated to the other nodes. In network investigation, identifying the community structure is crucial task, particularly for exposing connections between certain nodes. For community overlapping, network discovery, there are numerous methodologies described in the literature. Numerous scholars have recently focused on network embedding and feature learning techniques for node clustering. These techniques translate the network into a representation space with fewer dimensions. In this paper, a deep neural network-based model for learning graph representation and stacked auto-encoders are given a nonlinear embedding of the original graph to learn the model. In order to extract overlapping communities, an AEOCDSN algorithm is used. The efficiency of the suggested model is examined through experiments on real-world datasets of various sizes and accepted standards. The method outperforms various well-known community detection techniques, according to empirical findings
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
We propose a novel class of network models for temporal dyadic interaction
data. Our goal is to capture a number of important features often observed in
social interactions: sparsity, degree heterogeneity, community structure and
reciprocity. We propose a family of models based on self-exciting Hawkes point
processes in which events depend on the history of the process. The key
component is the conditional intensity function of the Hawkes Process, which
captures the fact that interactions may arise as a response to past
interactions (reciprocity), or due to shared interests between individuals
(community structure). In order to capture the sparsity and degree
heterogeneity, the base (non time dependent) part of the intensity function
builds on compound random measures following Todeschini et al. (2016). We
conduct experiments on a variety of real-world temporal interaction data and
show that the proposed model outperforms many competing approaches for link
prediction, and leads to interpretable parameters
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