Community Detection Framework Research Articles

A unified framework of semi-supervised community detection integrating network topology and node content

Detecting the community structure within networks is important because it aids in the analysis of complex networks. However, the existence of diverse complex structures in the network topology can limit the topological information’s ability to represent communities. For instance, the accuracy of many traditional methods based on topology alone decreases sharply when a link is missing or noisy. To solve the above-mentioned issues simultaneously, we propose a unified framework of semi-supervised community detection integrating content information. Three models are proposed, instead of a special model-handling problem. The framework used performs the same interpretation for a target problem. The inputs contain the adjacent and node content matrix. The proposed models describe the network topology using a non-negative matrix factorization (NMF) algorithm–based generative model or modularity maximization algorithm. Additionally, we use the equivalence theory of the pLSI, and NMF models the content information based on NMF. Community membership is also used as the relationship between communities and node contents. The models uniformly integrate content information and network topology. Further, a constraint matrix that depicts must-links is built by calculating the similarity of neighbors between any two nodes. Must-link pairs can represent the network topology’s prior information. The models’ community memberships are adjusted by using a strong constraint based on the connected component of this matrix. The models also introduce prior information into the topological and content information in the mentioned models. We compared the proposed models with the state-of-the-art community detection models on artificial networks and five real networks. The experimental results show that the proposed models outperformed the models combining topology and content and semi-supervised models. Fusing the topology with the content and the prior information in the models was also effective for community detection. Thus, the models under the framework are more advantageous than other semi-supervised models when incorporating the same amount of prior information into the models. The semi-supervised framework used also has a certain level of competitiveness in detecting communities.

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.

Interest-driven community detection on attributed heterogeneous information networks

Community structures within attributed heterogeneous information networks (AHINs) serve as valuable tools for comprehending the functional properties inherent in the real-world systems they mirror. The diverse semantics embedded in AHINs play a pivotal role in shaping distinct community formations. Many existing methods detect communities in AHINs based on the same-type nodes without specifying semantic meanings, resulting in unclear and potentially ambiguous outcomes. Additionally, by adopting a simple strategy that focuses solely on either graph structures or node attributes, they fail to sufficiently integrate heterogeneous information and maintain semantic consistency throughout the entire detection process. In this paper, we propose IDCD, a user-interest-driven community detection framework on AHINs, which groups heterogeneous nodes following specified user guidance for semantics-clear results. Firstly, we map heterogeneous nodes into one unified representation space by neatly integrating user-interest-related heterogeneous information. Then we assign pseudo community labels to nodes and refine them in a self-training way. These two processes are iteratively optimized and enhanced mutually in a unified framework. Extensive experiments demonstrate the superiority of IDCD.

Survey on graph neural network-based community detection and its applications

Detecting communities within a network is a critical component of network analysis. The process involves identifying clusters of nodes that exhibit greater similarity to each other compared to other nodes in the network. In the context of Complex networks (CN), community detection becomes even more important as these clusters provide relevant information of interest. Traditional mathematical and clustering methods have limitations in terms of data visualization and high-dimensional information extraction. To address these challenges, graph neural network learning methods have gained popularity in community detection, as they are capable of handling complex structures and multi-dimensional data. Developing a framework for community detection in complex networks using graph neural network learning is a challenging and ongoing research objective. Therefore, it is essential for researchers to conduct a thorough review of community detection techniques that utilize cutting-edge graph neural network learning methods [102], in order to analyze and construct effective detection models. This paper provides a brief overview of graph neural network learning methods based on community detection methods and summarizes datasets, evaluation metrics, applications, and challenges of community detection in complex networks.

Évolution de la revue Management international : Détection et analyse des communautés des articles publiés entre 2009 et 2023

This second research note draws from the same dataset of 829 abstracts and papers published by the journal over the period in question to formulate a community detection framework capable of decoding unstructured textual data, notwithstanding the complexities thereof. Using data science analysis tools, the research note discovers a number of hidden thematic groups, thereby spawning new perspectives on the various ways that journal authors have collaborated and evolved over this period. Several significant phases are identified, including data pre-processing and visualisation. Both research notes offer actionable insights and lay out a strategic roadmap enabling further editorial development.

A Framework for Accurate Community Detection on Signed Networks Using Adversarial Learning

In this paper, we propose a framework for embedding-based community detection on signed networks, namely <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>A</i></u> dversarial learning of <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>B</i></u> alanced triangle for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>C</i></u> ommunity detection, in short <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ABC}$</tex-math></inline-formula> . It first represents all the nodes of a signed network as vectors in low-dimensional embedding space and conducts a clustering algorithm ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means) on vectors, thereby detecting a community structure in the network. When performing the embedding process, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ABC}$</tex-math></inline-formula> learns only the edges belonging to balanced triangles whose edge signs follow the balance theory, significantly excluding noise edges in learning. To address the sparsity of balanced triangles in a signed network, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ABC}$</tex-math></inline-formula> learns not only the edges in balanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">real</i> -triangles but those in balanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">virtual</i> -triangles that do not actually exist but are produced by our generator. Finally, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ABC}$</tex-math></inline-formula> employs adversarial learning to generate more-realistic balanced virtual-triangles with less noise edges. Through extensive experiments using seven real-world networks, we validate the effectiveness of (1) learning edges belonging to balanced real/virtual-triangles and (2) employing adversarial learning for signed network embedding. We show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf ABC}$</tex-math></inline-formula> consistently and significantly outperforms the state-of-the-art community detection methods in all datasets.

A Hybrid Probabilistic Privacy Preserving Based Community Detection Model on Online Social Networking Data

Privacy preserving plays a vital role on the online social networking sites due to high dimensionality and data size. Community detection is used to find the social relationships among the node edges and links. However, most of the conventional models are difficult to process the community structure detection due to high computational time and memory. Also, these models require contextual weighted nodes information for privacy preserving process. In order to overcome these issues, an advanced probabilistic weighted based community detection and privacy preserving framework is developed on the large social networking data. In this model, a filter based probabilistic model is developed to remove the sparse values and to find the weighted community detection nodes and its profiles for privacy preserving process. Experimental results show that the filter based probabilistic community detection framework has better efficiency in terms of normalized mutual information, Q, rand index and runtime (ms).

Unified robust network embedding framework for community detection via extreme adversarial attacks

Graph data are widely available in complex network systems. Numerous community detection algorithms have been investigated to study graph problems wherein the network topology offers abundant behavioral and functional information. In real scenarios, networks are extremely susceptible to external perturbations, primarily because of sparse topological information. Consequently, maintaining robust community detection performance when confronted with intricate network attacks or perturbations is challenging. Previous anti-graph perturbation methods have typically been adjusted to specific perturbation types, leading to the degradation or failure of these defences when confronted with other types of attack. Accordingly, a unified robust framework that leverages extreme adversarial attacks is proposed in this paper. Specifically, a novel graph perturbation module is introduced into the model to generate extreme undirected or directed perturbations through dynamic updating; this ensures that the model can fit the attacked network well. The proposed framework derives a unified perturbation formulation capable of simultaneously attacking symmetric and asymmetric networks. Furthermore, this framework can be applied to many existing non-negative matrix factorization-based community detection methods. Extensive experiments on artificial and real-world networks demonstrate that the proposed framework significantly improves the robustness of detection tasks, particularly in noisy networks.

A multi-level generative framework for community detection in attributed networks

AbstractCommunity detection in attributed networks is one of the most important tasks in complex network analysis. Many existing methods propose to integrate the network topology and node attribute from a generative aspect, which models an attributed network as a probabilistic generation process with the community distribution described by hidden variables. Though they can provide good interpretability to the community structure, it is difficult to infer community membership quickly due to their high computational complexity when inferring. Motivated by the multi-level strategy, in this study, we propose a multi-level generative framework to reduce the time cost of generative models for community detection in attributed networks. We first coarsen an attributed network into smaller ones by node matching. Then, we employ the existing generative model on the coarsest network without any modification for community detection, thus efficiently obtaining community memberships of nodes in this small coarsest network. Last, we project the assignments back to the original network through a local refinement mechanism to get communities. Extensive experiments on several real-world and artificial attributed networks show that our multi-level-based method is significantly faster than original generative models and is able to achieve better or more competitive results.

Improvement the Community Detection with Graph Autoencoder in Social Network Using Correlation-Based Feature Selection Method

Background:&#x0D; In this paper, we aim to improve community detection methods using Graph Autoencoder. Community detection is a crucial stage in comprehend the purpose and composition of social networks.&#x0D; Materials and Methods:&#x0D; We propose a Community Detection framework using the Graph Autoencoder (CDGAE) model, we combined the nodes feature with the network topology as input to our method. A centrality measurement-based strategy is used by CDGAE to deal with the featureless dataset by providing artificial attributes to its nodes. The performance of the model was improved by applying feature selection to node features&#x0D; The basic innovation of CDGAE is that added the number of communities counted using the Bethe Hessian Matrix in the bottleneck layer of the graph autoencoder (GAE) structure, to directly extract communities without using any clustering algorithms.&#x0D; Results:&#x0D; According to experimental findings, adding artificial features to the dataset's nodes improves performance. Additionally, the outcomes in community detection were much better with the feature selection method and a deeper model. Experimental evidence has shown that our approach outperforms existing algorithms.&#x0D; Conclusion:&#x0D; In this study, we suggest a community detection framework using graph autoencoder (CDMEC). In order to take advantage of GAE's ability to combine node features with the network topology, we add node features to the featureless graph nodes using centrality measurement. By applying the feature selection to the features of the nodes, the performance of the model has improved significantly, due to the elimination of data noise. Additionally, the inclusion of the number of communities in the bottleneck layer of the GAE structure allowed us to do away with clustering algorithms, which helped decrease the complexity time. deepening the model also improved the community detection. Because social media platforms are dynamic.

Mapping nonlocal relationships between metadata and network structure with metadata-dependent encoding of random walks.

Integrating structural information and metadata, such as gender, social status, or interests, enriches networks and enables a better understanding of the large-scale structure of complex systems. However, existing approaches to augment networks with metadata for community detection only consider immediately adjacent nodes and cannot exploit the nonlocal relationships between metadata and large-scale network structure present in many spatial and social systems. Here, we develop a flow-based community detection framework based on the map equation that integrates network information and metadata of distant nodes and reveals more complex relationships. We analyze social and spatial networks and find that our methodology can detect functional metadata-informed communities distinct from those derived solely from network information or metadata. For example, in a mobility network of London, we identify communities that reflect the heterogeneity of income distribution, and in a European power grid network, we identify communities that capture relationships between geography and energy prices beyond country borders.

Community detection in weighted networks using probabilistic generative model

Community detection in networks is a useful tool for detecting the behavioral and inclinations of users to a specific topic or title. Weighted, unweighted, directed, and undirected networks can all be used for detecting communities depending on the network structure and content. The proposed model framework for community detection is based on weighted networks. We use two important and effective concepts in graph analysis. The structural density between nodes is the first concept, and the second is the weight of edges between nodes. The proposed model advantage is using a probabilistic generative model that estimates the latent parameters of the probabilistic model and detecting the community based on the probability of the presence or absence of weighted edge. The output of the proposed model is the intensity of belonging each weighted node to the communities. A relationship between the observation of a pair of nodes in multiple communities and the probability of an edge with a high weight between them, is one of the important outputs that interpret the detected communities by finding relevancy between membership of nodes to communities and edge weight. Experiments are performed on real-world weighted networks and synthetic weighted networks to evaluate the performance and accuracy of the proposed algorithm. The results will show that the proposed algorithm is more density and accurate than other algorithms in weighted community detection.

Seed Community Identification Framework for Community Detection over Social Media.

The social media podium offers a communal perspective platform for web marketing, advertisement, political campaign, etc. It structures like-minded end-users over the explicit group as a community. Community structure over social media is the collaborative group of globally spread users having similar interests regarding a communal topic, product or any other axis. In recent years, researchers have widely used clustering techniques of data mining to structure communities over social media. Still, due to a lack of network and implicit communal information, researchers cannot bind mutually robust and modular community structures. The collaborative features of social media are inherent with implicit and explicit end-users. The explicit nature of both active and passive users is easily extracted from the graphical structure of social media. On the other hand, the degree of information inclusion of implicit features depends upon end-users participation. The Implicit features of frequently active users are diversely available, while integrating passive and silent users’ implicit features over the community is tedious. This work proposed a social theory based influence maximization (STIM) framework for community detection over social media. It combines user-generated content with profile information, extracts passive social media users through influence maximization, and provides the user space for influencing inactive users. The STIM framework clusters identical nodes over the maximum influencing node axis based on their graphical parameters such as node degree, node similarity, node reachability, modularity, and node density. This framework also provides the structural, relational and mathematical concept for the functional grouping of like-minded people as a community over social media through social theory. Finally, an evaluation has been carried out over six real-time datasets. It analyses that convolution neural network over STIM structure more dense and modular communities via influence maximization. STIM acquired around 93% modularity and 94% Normalized Mutual Information (NMI), resulting in approximately 2.23% and 5.69% improvements in modularity and NMI, respectively, over the best-acquired result of the benchmark approach.

Multi-Purpose function-based Parliamentary Optimization Framework for Community Detection over social media

This paper presents a multi-purpose function-based Parliamentary Optimization, community detection methods. Initially, the population of Parliamentary Optimization Algorithm was created in a Python environment for the data used. The population formed was divided into a certain number of groups, and the power values of each group were calculated. While strong groups show joining according to the determined combination probability value, the vulnerable groups are eliminated from the population according to the determined deletion probability. The result for the problem has been approached. The program steps continued until all groups were combined and the termination condition of the algorithm was met; the individual with the highest eligibility values among the remaining data in the last stage was accepted as the solution to the overlapping community discovery problem of the proposed algorithm. Subsequently, performs a proposed work evaluated over one artificial and four real network-based social media data sets. The comparison of feature evaluation is carried out to identify the influence of Single and Multi-Purpose functions on community detection performance. Finally, this paper gives a comparative analysis of proposed MPPOA algorithm worth three heuristic overlap community detection algorithm over six real social media data set.

Comparative Analysis of Overlap Community Detection Techniques on Social Media Platform

Abstract Community structure over social media (SM) is the collaborative group of globally spread users with identical characteristics and ideologies. The collective features of SM are inherent with both the implicit and explicit nature of end-users. This paper presents an analytical and methodological community detection framework to bind passive users’ implicit and explicit nature after scrutinizing graphical data to identify seed nodes and communities. Moreover, this work provides the concept of the unsupervised machine learning approach over the graphical perspective of SM to identify the trade-off between similarity of nodes attributes and density of connections for social theories. Subsequently, this paper evaluates a comprehensive analysis of the benchmark community detection algorithm (CDA) Label Propagation Algorithm (LPA), Clique Percolation Method (CPM), Democratic Estimate of the Modular Organization of a Network (DEMON) and Non-Negative Matrix Factorization (NMF). The evaluation has been carried out over modularity and normalized mutual information of resultant structured community on six real-time SM data set. The performance of benchmark CDAs is significantly increased after incorporating social theories. NMF, DEMON, CPM and LPA gained the highest improvement over Zachary’s Karate Club data sets, i.e. approximate 26.91%, 21.68%, 18.79%, 19.96%, respectively.

A Survey of Community Detection in Complex Networks Using Nonnegative Matrix Factorization

Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

Dynamic community detection based on the Matthew effect

The identification of community structures plays a crucial role in analyzing network topology, exploring network functions, and mining potential patterns in complex networks. Many algorithms have been proposed for identifying community structures in static networks from different perspectives. However, most networks in the real world are not static and their structures constantly evolve over time. Identifying community structures in dynamic networks remains a challenging task because of the variability, complexity, and large scale of dynamic networks. In this study, we propose a framework and Matthew effect model for community detection in dynamic networks. Based on this architecture and model, we design a dynamic community detection algorithm called, Dynamic Community Detection based on the Matthew effect (DCDME), which employs a batch processing method to reveal communities incrementally in each network snapshot. DCDME has several desirable benefits: high-quality community detection, parameter-free operation, and good scalability. Extensive experiments on synthetic and real-world dynamic networks have demonstrated that DCDME has many advantages and outperforms several state-of-the-art algorithms.

DICO: A Graph-DB Framework for Community Detection on Big Scholarly Data

The widespread use of online social networks has also involved the scientific field in which researchers interact each other by publishing or citing a given paper. The huge amount of information about scientific research documents has been described through the term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Big Scholarly Data</i> . In this article we propose a framework, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Discovery Information using COmmunity detection</i> (DICO), for identifying overlapped communities of authors from Big Scholarly Data by modeling authors’ interactions through a novel graph-based data model combining jointly document metadata with semantic information. In particular, DICO presents three distinctive characteristics: i) the coauthorship network has been built from publication records using a novel approach for estimating relationships weight between users; ii) a new community detection algorithm based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Node Location Analysis</i> has been developed to identify overlapped communities; iii) some built-in queries are provided to browse the generated network, though any graph-traversal query can be implemented through the Cypher query language. The experimental evaluation has been carried out to evaluate the efficacy of the proposed community detection algorithm on benchmark networks. Finally, DICO has been tested on a real-world Big Scholarly Dataset to show its usefulness working on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DBLP+AMiner</i> dataset, that contains 1.7M+ distinct authors, 3M+ papers, handling 25M+ citation relationships.

MSN‐CDF: New community detection framework to improve routing in mobile social networks

SummaryIn mobile social networks (MSN), portable devices of mobile users communicate with each other. The contact and intercontact times are the two most important parameters that specify the behavior of MSN. Many social features such as similarity, centrality, closeness, and distance are derived from contact and intercontact time patterns. Those parameters and features can be used in the community detection process in community‐based routing as a vector weight of network graph edges. But there is no solution for proper exploitation of them as a multicriteria optimization of community detection algorithms so that the communities become more adaptive with the MSN quiddity and the computational complexity of the algorithms to be stay constant. In this paper, we propose the MSN‐adaptive community detection framework, MSN‐CDF, which meets these demands. We improve the quality function of community detection methods, for example, the Intensity in clique percolation methods and the Modularity in modularity‐based methods, through our new model‐based joint optimization of their quality functions. To this end, to express the MSN contact and social features, we develop a criterion called MCSF. It holds the contact time and intercontact time distributions parameters and implies their social features for MSN purposes. This criterion has as little traffic as possible and enjoys from the probabilistic joint distribution model. The simulations show that MSN‐CDF reduces the network overhead significantly compared with the DiBUBB, Epidemic, and Dynamic Routing. Moreover, it outperforms DiBUBB and Dynamic Routing in terms of delivery rate and delivery latency.

IbLT: An effective granular computing framework for hierarchical community detection

IbLT: An effective granular computing framework for hierarchical community detection