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…
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48 results for “community detection”
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.
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.
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.
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…
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…
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.
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…
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.
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…
Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate …
Paper studies community detection in censored hypergraphs using information theory.
problem Community detection in censored hypergraphs with missing values.
method Information-theoretic approach, polynomial-time algorithm, spectral algorithm with refinement.
result Derives information-theoretic threshold for exact recovery of community structure.
The paper introduces curvature-based clustering algorithms for graph analysis.
problem Identifying densely connected substructures in graphs for community detection.
method Discrete Ricci curvatures and geometric flows to reveal community structure.
result The curvature-based approach can identify overlapping communities in graphs.
New method detects overlapping communities in weighted graphs without pure nodes assumption.
problem Detect overlapping communities in weighted graphs without making pure nodes assumption.
method Convex optimization-based approach for weighted graphs.
result Success on artificial and real-world datasets.
Paper proposes a method to detect fair communities in graphs considering demographic attributes.
problem Inconsistent community detection violates fairness constraints for nodes with demographic attributes.
method Develops an -regularized pseudo-likelihood approach for fair graphical model selection.
result The method ensures demographic groups are fairly represented within detected communities.
Much of the community detection literature studies structural communities, communities defined solely by the connectivity patterns of the network. Often, networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network. In this work, we i…
Improved community detection in graphs with probabilistic models.
problem Lack of probabilistic formulation and fixed number of communities in GNN-based methods.
method Combines GNNs with amortized clustering for variable numbers of clusters.
result Improved performance on synthetic and real datasets compared to previous methods.
A fast spectral algorithm detects community structure in evolving graphs.
problem Detecting community structure in time-evolving sparse graphs.
method Extension of the Bethe-Hessian matrix for spectral community detection.
result The algorithm reaches the optimal detectability threshold and outperforms other methods.
GNNs robustness in community detection is studied with various perturbations.
problem Understanding GNNs robustness in community detection tasks.
method Systematic computational evaluation of six GNN architectures on synthetic and real-world networks.
result Supervised GNNs achieve higher baseline accuracy, while DMoN shows stronger resilience to perturbations.
Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approachcs.LG
A new method detects communities in multi-relational networks.
problem Detect communities in heterogeneous multi-relational networks.
method Message passing based algorithm on a hierarchical structure of homogeneous networks.
result Effectiveness of the proposed method confirmed on synthetic and real-world networks.
The methodology of community detection can be divided into two principles: imposing a network model on a given graph, or optimizing a designed objective function. The former provides guarantees on theoretical detectability but falls short when the graph is inconsistent with the underlying model. The latter is model-fre…
VEC-SBM detects communities using side information like texts and images.
problem Community detection in social networks with side information.
method Proposes a novel algorithm based on iterative refinement techniques.
result Optimally recovers latent communities with side information.
Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detectionstat.ML
Study information limits for community detection in sub-hypergraphs.
problem Identify limits for exact community detection in sub-hypergraphs.
method Use Fano's inequality to define model parameters and identify success and failure regions.
result Identify regions where algorithms succeed or fail in exact recovery.
Simulated Bifurcation outperforms quantum machines in community detection.
problem Community detection in complex networks
method Quantum-inspired Simulated Bifurcation algorithm for QUBO formulation
result Simulated Bifurcation achieves highest modularity in community detection
A new method speeds up community detection in graphs.
problem Efficiently detecting communities in large graphs.
method Subsampled semidefinite programming for faster clustering.
result Statistical limits of sketching for community detection established.
Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, fee…
Detecting communities on graphs has received significant interest in recent literature. Current state-of-the-art community embedding approach called \textit{ComE} tackles this problem by coupling graph embedding with community detection. Considering the success of hyperbolic representations of graph-structured data in …
This paper studies the problem of detecting the presence of a small dense community planted in a large Erdős-Rényi random graph , where the edge probability within the community exceeds by a constant factor. Assuming the hardness of the planted clique detection problem, we show that the computatio…
It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In this paper, we extend adversarial graphs to the problem of community detection which is much more difficult. We focus on black-box attack and …
Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However struc…
This paper employs the extrinsic information transfer (EXIT) method, a technique imported from the analysis of the iterative decoding of error control codes, to study the performance of belief propagation in community detection in the presence of side information. We consider both the detection of a single (hidden) com…
New algorithm detects community labels in networks using unlabeled data.
problem Detect community labels in networks with partially labeled data.
method Proposes an algorithm using structural similarity metrics.
result Theoretical guarantees for misclassification error.
DFM model detects communities in weighted networks without distributional assumptions.
problem Challenges in community detection for weighted networks.
method Distribution-Free Model (DFM) for weighted networks, using spectral clustering.
result Spectral clustering yields consistent community detection under DFM.
Sketch-based community detection in evolving networksphysics.soc-ph
Sketch-based approach detects community events in evolving networks.
problem Community detection in time-varying networks.
method Maintains a small sketch graph to capture essential community structure.
result Efficiently identifies six key community events during network evolution.
New method for community detection in graphs faster than DCBM inference.
problem Efficiently detecting communities in graphs with heterogeneous node degrees.
method Reformulated constrained nonnegative matrix factorization for DCBM inference.
result Faster community detection (4 minutes for 100k nodes vs. DCBM's 10+ minutes).
A Health Service Area (HSA) is a group of geographic regions served by similar health care facilities. The delineation of HSAs plays a pivotal role in the characterization of health care services available in an area, enabling a better planning and regulation of health care services. Though Dartmouth HSAs have been the…
Optimal distributed composite testing in high-dimensional Gaussian models with 1-bit communicationcs.IT
Paper studies signal detection in noisy environments with limited communication.
problem Signal detection in Gaussian noise with 1-bit communication constraints.
method Derives lower bounds and exhibits optimal testing strategies.
result Optimal distributed testing strategies attain the derived lower bound.