Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilises the Bayesian non-negative matrix factorisation (NMF) model to produce a probabilistic output for node memberships. The scheme has the advantage of computational efficiency…
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48 results for “Bayesian community detection”
Bayesian model detects communities in networks with covariates.
problem Extracting meaningful community clusters from network data with covariates.
method Proposes a Bayesian stochastic block model with a covariate-dependent random partition prior.
result Can learn the number of communities via posterior inference.
Bayesian methods detect clusters in noisy data more reliably.
problem Noisy data distorts traditional clustering methods, leading to unreliable results.
method Bayesian community detection using Minimum Description Length principle.
result Bayesian methods identify more robust clusters in noisy data.
A new SBM for bipartite networks improves community detection in noisy data.
problem Community detection in bipartite networks with stochastic blockmodels.
method Bayesian nonparametric formulation of SBM for bipartite networks, algorithm to find communities efficiently.
result Improves community detection results over general SBMs, especially in noisy data.
Community detection has been an active research area for decades. Among all probabilistic models, Stochastic Block Model has been the most popular one. This paper introduces a novel probabilistic model: RW-HDP, based on random walks and Hierarchical Dirichlet Process, for community extraction. In RW-HDP, random walks c…
Bayesian method detects mesoscale structures in pathway data networks.
problem Mesoscale structures in pathway data networks are hard to detect due to dependencies between interactions.
method Bayesian approach modeling optimal partitioning and higher-order dynamics.
result Method can recover both proximity-based and role-based groupings of nodes.
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, particul…
The paper analyzes distributed Bayesian inference and its Frequentist guarantees.
problem Analyzing large decentralized datasets with distributed Bayesian inference.
method Establishes Frequentist properties for distributed (non-)Bayesian inference.
result Distributed Bayesian inference retains parametric efficiency and enhances robustness.
We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform…
Paper proposes a new MIMO detection algorithm using Gaussian Mixture Expectation Propagation.
problem Challenges in MIMO detection due to interference and noise in high-order high-dimensional systems.
method The approach uses a Gaussian Mixture Model (GMM) approximation for Belief Propagation (BP) and Expectation Propagation (EP) messages to improve detection accuracy.
result The proposed algorithm outperforms state-of-the-art detection algorithms while maintaining low computational complexity.
Method infers assortative communities in networks without resolution limit.
problem Finding statistically significant assortative modules in networks.
method Nonparametric Bayesian formulation of the planted partition model.
result Method uncovers an arbitrarily large number of communities with statistical evidence.
We propose an efficient meta-algorithm for Bayesian estimation problems that is based on low-degree polynomials, semidefinite programming, and tensor decomposition. The algorithm is inspired by recent lower bound constructions for sum-of-squares and related to the method of moments. Our focus is on sample complexity bo…
Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network, with strong consistency results in the stoch…
A new model clusters networks with community-specific submanifold structures.
problem Clustering networks with community-specific submanifold structures.
method Latent Structure Block Models (LSBM) for Bayesian spectral graph clustering.
result LSBM correctly recovers underlying communities in one-dimensional manifold structures.
A common data mining task on networks is community detection, which seeks an unsupervised decomposition of a network into structural groups based on statistical regularities in the network's connectivity. Although many methods exist, the No Free Lunch theorem for community detection implies that each makes some kind of…
Model clusters networks and their communities simultaneously.
problem Clustering networks and their communities in unlabeled, heterogeneous networks.
method Nested Stochastic Block Model (NSBM) with Bayesian approach and NDP prior.
result Model accurately estimates both within and across network clustering structures.
Statistical estimates can often be improved by fusion of data from several different sources. One example is so-called ensemble methods which have been successfully applied in areas such as machine learning for classification and clustering. In this paper, we present an ensemble method to improve community detection by…
Community detection improves stock market portfolio optimization.
problem Improving portfolio optimization in financial markets.
method Community detection in correlation-based networks of worldwide stock markets.
result Portfolios constructed using community detection outperform traditional methods.
A hierarchical gamma process infinite edge partition model is proposed to factorize the binary adjacency matrix of an unweighted undirected relational network under a Bernoulli-Poisson link. The model describes both homophily and stochastic equivalence, and is scalable to big sparse networks by focusing its computation…
Stochastic blockmodels and variants thereof are among the most widely used approaches to community detection for social networks and relational data. A stochastic blockmodel partitions the nodes of a network into disjoint sets, called communities. The approach is inherently related to clustering with mixture models; an…
Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimesstat.ML
Paper proposes a method to improve variational inference for sparse networks.
problem Variational inference struggles with sparse networks, leading to inaccurate community detection.
method The method involves hard thresholding the posterior of community assignment after each iteration.
result The proposed method accurately recovers true community labels in sparse networks.
Study exact community detection in k-community Gaussian mixtures with different intensities.
problem Community detection in k-community Gaussian mixtures with varying intensities.
method Explicitly find the threshold for exact recovery of maximum likelihood estimation.
result Threshold for exact recovery of maximum likelihood estimation is identified.
Graph energy helps detect communities in networks better than traditional methods.
problem Detecting communities in sparse networks where traditional methods fail.
method Using graph energy based on the full spectrum of adjacency matrices.
result The difference in graph energy between a planted partition model and an Erdős--Rényi network has a distinct transition at the detectability threshold.
Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relatively small subgraph in an enormous, potentially uninteresting background. This aspect characterizes network detection as …
Real-world networks usually have community structure, that is, nodes are grouped into densely connected communities. Community detection is one of the most popular and best-studied research topics in network science and has attracted attention in many different fields, including computer science, statistics, social sci…
Spectral clustering with edge counting detects communities in sparse models.
problem Detecting communities in sparse latent space models.
method Spectral clustering followed by edge counting.
result Algorithm achieves consistency and optimality for a broad class of models.
Community detection is a central problem of network data analysis. Given a network, the goal of community detection is to partition the network nodes into a small number of clusters, which could often help reveal interesting structures. The present paper studies community detection in Degree-Corrected Block Models (DCB…
Efficient method for vertex embedding and community detection.
problem Vertex embedding and community detection.
method Normalized one-hot graph encoder and rank-based cluster size measure.
result Excellent numerical performance of graph encoder ensemble algorithm.
A new probabilistic model detects communities in networks using both structure and node features.
problem Detecting communities in networks with node features for more accurate results.
method Generative probabilistic model considering network structure and node features.
result The model accurately detects communities and determines feature strength.
Local network community detection aims to find a single community in a large network, while inspecting only a small part of that network around a given seed node. This is much cheaper than finding all communities in a network. Most methods for local community detection are formulated as ad-hoc optimization problems. In…
Paper shows how to hide individuals in graphs to fool community detection models.
problem Adversarial attack on community detection models by hiding individuals.
method Iterative learning framework that updates a graph generator and a community detection model.
result Adversarial graphs generated by the method can fool multiple community detection models.
Flexible inference model for multilayer networks with heterogeneous data.
problem Complexity of heterogeneous data in networked datasets.
method Probabilistic generative model using Bayesian framework and Laplace matching.
result Effective detection of overlapping community structures and prediction tasks.
The paper characterizes brain states and transitions using functional MRI data.
problem Characterizing the dynamic reconfiguration of neural systems in brain states.
method Bayesian model-based characterization of latent brain states and posterior predictive discrepancy using the latent block model.
result The model detects transitions between latent brain states and identifies distinctive community patterns in task-fMRI data.
A new model detects complex network communities using node attributes.
problem Lack of methods integrating node attributes for community detection in attributed networks.
method BCSBM model that integrates betweenness centrality and clustering coefficient of nodes.
result BCSBM model outperforms other methods in detecting various network structures.
In this paper, we study the sensitivity of the spectral clustering based community detection algorithm subject to a Erdos-Renyi type random noise model. We prove phase transitions in community detectability as a function of the external edge connection probability and the noisy edge presence probability under a general…
Paper forecasts dynamic transportation networks using probabilistic models.
problem Forecasting temporal evolution of transportation networks.
method Probabilistic latent network model with Bayesian inference.
result Models accurately predict future network states and community structures.
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 commun…
Community detection is a fundamental problem in machine learning. While deep learning has shown great promise in many graphrelated tasks, developing neural models for community detection has received surprisingly little attention. The few existing approaches focus on detecting disjoint communities, even though communit…
Implicit models, latent compression, intrinsic biases, and cheap lunches in community detectioncs.SI
New method compares community detection algorithms without ground truth.
problem Comparing community detection algorithms without ground truth.
method Integrates any objective with its implicit generative model.
result More expressive methods consistently outperform others.
We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, t…
This paper improves hierarchical community detection efficiency using local structural properties.
problem Efficiency of hierarchical community detection methods in large networks.
method Use of local structural network properties as proxies to improve efficiency.
result Achieves competitive results in modularity with improved efficiency.
This study compares community detection algorithms for delineating health service areas.
problem Delineating health service areas to improve health care services.
method Comparative analysis of community detection algorithms on hospital-patient discharge networks.
result Infomap algorithm produced the best delineation of health service areas.
The paper introduces a method for detecting principal communities and embedding vertices.
problem Detecting and embedding vertices in graphs with community structure.
method Principal graph encoder embedding method that detects principal communities and produces vertex embeddings.
result The method successfully detects principal communities and produces accurate vertex embeddings.
Analyzes methods for detecting communities in networks.
problem Detecting communities in networks.
method Examined graph theory, reviewed community detection methods, classified them, and evaluated community quality.
result Developed a classification of community detection methods and evaluated their strengths and weaknesses.
Many methods have been proposed for community detection in networks, but most of them do not take into account additional information on the nodes that is often available in practice. In this paper, we propose a new joint community detection criterion that uses both the network edge information and the node features to…
Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practioners to determine which method would be suitable to get insights into the structural information of…
Improved spectral clustering for community detection in networks.
problem Community detection in networks.
method Improved spectral clustering (ISC) based on k-means clustering on weighted eigenvectors of a regularized Laplacian matrix.
result ISC yields stable consistent community detection under mild conditions and outperforms classical methods.
Grouping objects into clusters based on similarities or weights between them is one of the most important problems in science and engineering. In this work, by extending message passing algorithms and spectral algorithms proposed for unweighted community detection problem, we develop a non-parametric method based on st…