Overlapping Community Detection Research Articles

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study.

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

A generalized stochastic block model for overlapping community detection

Abstract Over the past two decades, community detection has been extensively explored. Yet, the challenge of identifying overlapping communities remains unresolved. In this letter, we introduces a novel approach called the generalized stochastic block model, which addresses this issue by allowing nodes to belong to multiple communities. This approach extends the traditional representation of nodal community assignment from a single community label to a label vector, with each element indicating the membership of a node in a specific community. To tackle this model, we develop a Markov Chain Monte Carlo algorithm. Through numerical experiments conducted on synthetic and empirical networks, we demonstrate the efficacy of our proposed framework in accurately detecting overlapping communities.

A semi-orthogonal nonnegative matrix tri-factorization algorithm for overlapping community detection

A semi-orthogonal nonnegative matrix tri-factorization algorithm for overlapping community detection

Module partition for complex products based on stable overlapping community detection and overlapping component allocation

Module partition for complex products based on stable overlapping community detection and overlapping component allocation

Evolutionary multi-objective overlapping community detection based on fusion of internal and external connectivity and correction of node intimacy

In the field of community detection, node attribute information plays an important role in community division. Existing methods use topology structure and node attribute information to discover non-overlapping communities. However, so far, attribute information has not been fully utilized in overlapping community detection. To address this, we propose a new overlapping community detection method called “evolutionary multi-objective overlapping community detection based on Fusion of internal and external Connectivity and Correction of Node Intimacy” (FCCNI). Firstly, we propose a fusion strategy based on internal and external connectivity, which integrates some communities with sparse intra-connections and dense inter-connections. This automatically determines, reconfirms, and corrects the number of communities. Secondly, a function is designed to calculate the intimacy between nodes, and the node label with the highest intimacy is selected to correct the current wrong node. The correction strategy is used in two stages of initialization and multi-objective evolution to obtain a more accurate node label. Finally, a method that considers not only the connections of the community, but also the node attribute, is designed to obtain the overlapping community indirectly from the non-overlapping community. The experimental results on five real-life networks and four classical synthetic networks show that FCCNI achieves better overlapping community division, compared with six state-of-the-art comparison algorithms from the literature.

A hierarchical overlapping community detection method based on closed trail distance and maximal cliques

An important feature of real networks is their hierarchy and the existence of overlapping communities. Hierarchical agglomerative clustering is one way to determine the hierarchy of a network. To ensure the existence of overlapping communities, it is appropriate to choose the base elements for clustering – edges, cliques, etc. These base elements can then have common vertices and naturally provide the possibility of overlap. The proposed community detection method uses hierarchical agglomerative clustering on the 2-edge-connected component of the graph. Communities are constructed from maximal cliques as base elements. Novel dissimilarities for hierarchical agglomerative clustering were introduced for the merging of cliques. The dissimilarities use the size of the overlapped cliques and closed trail distance to express dissimilarity between communities in networks. The single linkage approach contains and extends the results of k-CPM. The proposed algorithm utilizing deterministic dissimilarity achieves comparable or superior outcomes compared to standard algorithms used for hierarchical or overlapping community detection.

Analysis of the Pertinence of Indian Women’s Institutions in Collaborative Research

Nowadays, the relevance of women-only educational institutes is questionable due to various reasons that include social uplifting, ample opportunities, and a free mindset among the population. Though women colleges and universities bring many female students to the purview of the traditional education system every year, other activities like research works and collaborative projects are still a concern for the educationists. In the current manuscript, the role of leading women-only universities and institutions is analyzed throughout India, to scrutinize its impact and opportunities in the research domain. All types of educational institutions and collaborative research work among them are represented as a social network. Different centrality metrics, such as closeness, betweenness, and eigenvector, are utilized to examine the influence of the individual institutes in the collaborative research network. An overlapping community detection method is proposed to perceive the posture of gender-biased universities in the mixed institutional model. The supremacy of the proposed approach is presented over the real-life benchmark datasets. The analytical results exhibit notable improvement over the state-of-the-art approaches and unfold a contemporary framework for strategic analysis in the higher education sector.

Advanced Intrusion Detection in Vehicular Networks: Empowering Security through Hybrid Off-loading Techniques and Enhanced Radial Bias Neural Network

Over the last several decades, the implementation of ITS has shown to be the most efficient and successful strategy for expanding the variety of current transportation networks. Vehicle-based offloading of data going to be essential for forthcoming networking innovations like D2D and 5G due to the substantial contribution it makes to efficiently using network capability while wasting minimal power. Information transmissions that would normally need a cellular network's infrastructure may instead be made using alternative networking mechanisms including Bluetooth, WiFi, and opportunistic communications. Data offloading has the ability to significantly increase the efficiency with which network resources are used. The offloading of data from vehicles has a considerable impact on the strain on cellular networks. It helps the network achieve higher throughput by facilitating the simultaneous reception of data by a large number of users. First, we must establish that the problem of Vehicular data offloading is an NP-hard target set selection (TSS) issue before we can even begin to characterize it. Using a combination of Hybrid PSO and GWO, TSS selects a small group of nodes to do the redundant data exchange (Particle Swarm Optimization with Gray Wolf Optimization). Collaboration between individuals and ISPs to identify effective aim sets may provide useful insights. If malicious users are present in the target group, they may slow down network activity by spoofing or by reducing the network's offloading capacity. It is possible that the whole network's performance would suffer as a direct result of these malicious users. In this study, we suggest a hybrid approach to communication for specifying the intended audience. We take use of the characteristics of opinion dynamics amongst users to get around the issue of overlapping community detection. Trust-based metrics inferred from users' activities are used to ensure the safety of the target set. In order to call 911, the suggested work additionally incorporates a method of sorting and classifying the offload limitations through Radial Bias Neural Network (RBNN). The following may be determined with the use of the proposed work's performance indicators: precision, entropy, and delay.

Overlapping Community Detection Based on Weak Equiconcept

Overlapping Community Detection Based on Weak Equiconcept

Temporal Dual-Attributed Network Generation Oriented Community Detection Model

Community detection is a crucial task on the research field of network analysis. However, this task recently has become challenging due to the explosion of network in terms of the scale and the side information, e.g., temporal information and attribute information. Here we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PGMTAN</i> —a probabilistic generative model for overlapping community detection on temporal dual-attributed networks. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PGMTAN</i> aims to characterize four generation processes: 1) generation of occurrence of the links, 2) generation of node-community memberships via assortative attributes, 3) generation of generative attributes, and 4) generation of evolutionary dynamics of community structure. Particularly, we adopt a hidden Markov chain model to capture the network's dynamics on the evolution of community structure over time. Moreover, we seek to optimize a lower-bound of likelihood function to accelerate the model's parameter estimation. We carry out extensive experiments on several real-world and synthetic networks to test <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PGMTAN</i> 's performance and the results substantiate that it can outperform multiple baselines and give us promising performance in terms of detection accuracy and convergence.

Retracted: Overlapping Community Detection Based on Node Importance and Adjacency Information

Retracted: Overlapping Community Detection Based on Node Importance and Adjacency Information

A new approach to finding overlapping community structure in signed networks based on Neutrosophic theory

Abstract One of the longstanding challenges in network science is the identification of overlapping community structures. Real-world networks often exhibit a complex interplay of positive and negative relationships, making the recognition of overlapping communities a crucial area of research. Current community detection methods in signed networks primarily focus on discovering disjoint communities, where each node belongs exclusively to a single community. However, these algorithms often fail to detect overlapping communities, where nodes can belong to multiple communities simultaneously. To address this limitation, we propose a novel approach called Neutrosophic c-means Overlapping Community Detection (NOCD) based on neutrosophic set (NS) theory. By incorporating NS theory, our approach effectively handles the uncertainty associated with ambiguous community boundaries and appropriately handles nodes on the community boundaries and isolated nodes. The NOCD method comprises two phases: firstly, a signed graph convolutional neural network is employed to learn the structural features of the signed network in a lower-dimensional representation; secondly, overlapping communities are detected using the neutrosophic c-means algorithm applied to the embedded network. To evaluate the effectiveness of our proposed NOCD method, we conducted comprehensive experiments on both real and artificial networks. The experimental results demonstrate the effectiveness and robustness of NOCD in identifying overlapping communities, outperforming existing methods. [Received on 3 August 2023; editorial decision on 13 December 2023; accepted on 19 December 2023]

Semantic overlapping community detection with embedding multi-dimensional relationships and spatial context

Semantic overlapping community detection with embedding multi-dimensional relationships and spatial context

A novel overlapping community detection strategy based on Core-Bridge seeds

A novel overlapping community detection strategy based on Core-Bridge seeds

Triangle-oriented Community Detection Considering Node Features and Network Topology

The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a triangle-based quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both non-overlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect.

Overlapping community detection using expansion with contraction

Numerous disjoint community detection methods have reached the state-of-the-art. Some overlapping community detection methods have been proposed in recent years, but they lack the ability to adjust the degree of overlap while maintaining detection quality. To well handle this issue, we in this paper propose a novel method, namely expansion with contraction method for overlapping community detection (ECOCD). Specifically, ECOCD obtains the disjoint communities through non-negative matrix factorization and proceeds to expansion with contraction process (including the expansion process and the contraction process). In each iteration of the process, we randomly select a community and then continuously conduct the expansion and contraction processes on this community. The former process absorbs nodes by the degree of affiliation that is newly defined, while the latter removes nodes by permanence. Moreover, we theoretically analyze the computational complexity of ECOCD. The advantage of ECOCD is that it is applicable to various networks with different properties by adjusting the degree of overlap, and enjoys high quality of overlapping community detection as well. Our experiments on both synthetic and real-world networks further verify this. Extensive experiments show that ECOCD is superior to the eleven state-of-the-art overlapping community detection methods in terms of four metrics, validating the effectiveness, efficiency and robustness of ECOCD.

Overlapping community detection in software ecosystem based on pheromone guided personalized PageRank algorithm

Software ecosystem has aroused the interest of numerous researchers and plays an important role in many aspects. According to different participants and their relationships, software ecosystem can be constructed into various types of complex networks. Overlapping community detection in complex networks can help reveal the community structure and find the intersection between communities. However, existing overlapping community detection algorithms often suffer from reduced applicability or accuracy when applied to networks in software ecosystems. To reveal the overlapping community structure in software ecosystem, we propose an overlapping community detection algorithm by improving the standard personalized PageRank (PPR) algorithm. We first construct a developer collaboration network in software ecosystem based on the intensity of cooperation between developers. Then, the similarity between developers is calculated to guide the walking process of the PPR algorithm, making it suitable for weighted networks. Finally, in the proposed algorithm PGPPR, inspired by the idea of using pheromones to guide the walking in ant colony algorithm, we run the algorithm in multiple rounds and use the results of the previous round to guide the walking process in the current round to reduce redundant diffusion. The experimental results on the five real-world networks show that our algorithm is applicable and effective in detecting communities. And in the five developer collaboration networks, PGPPR can effectively detect overlapping community structures with higher stability and accuracy than the four baselines. The PGPPR algorithm can find overlapping community structures in weighted networks and effectively reduce the redundant diffusion generated when applying the standard PPR algorithm to community detection. Compared to other algorithms, our algorithm can detect the overlapping communities more accurately and stably when applied to developer collaboration networks in software ecosystem.

CoDiS: Community Detection via Distributed Seed Set Expansion on Graph Streams

Community detection has been (and remains) a very important topic in several fields. From marketing and social networking to biological studies, community detection plays a key role in advancing research in many different fields. Research on this topic originally looked at classifying nodes into discrete communities (non-overlapping communities) but eventually moved forward to placing nodes in multiple communities (overlapping communities). Unfortunately, community detection has always been a time-inefficient process, and datasets are too large to realistically process them using traditional methods. Because of this, recent methods have turned to parallelism and graph stream models, where the edge list is accessed one edge at a time. However, all these methods, while offering a significant decrease in processing time, still have several shortcomings. We propose a new parallel algorithm called community detection with seed sets (CoDiS), which solves the overlapping community detection problem in graph streams. Initially, some nodes (seed sets) have known community structures, and the aim is to expand these communities by processing one edge at a time. The innovation of our approach is that it splits communities among the parallel computation workers so that each worker is only updating a subset of all the communities. By doing so, we decrease the edge processing throughput and decrease the amount of time each worker spends on each edge. Crucially, we remove the need for every worker to have access to every community. Experimental results show that we are able to gain a significant improvement in running time with no loss of accuracy.

An approach to co-medication mechanism mining of Chinese Materia Medica and western medicines based on complex networks with the multi-source heterogeneous information

The co-medication strategy of Chinese Materia Medica (CMM) and western medicines under the holistic integrative medicine (HIM) model offers the advantages of increased efficacy and reduced toxicity, particularly in clinical practice for treating complex and challenging diseases. However, due to the mutual fragmentation of basic theories and principles of medication decision-making between traditional Chinese medicine (TCM) and western medicine, research on the mechanisms for the combined use of CMM and western medicines has become a challenging problem in modern clinical medicine research. Traditional drug combination mining models encounter numerous bottlenecks when dealing with this complex decision-making problem with multi-source information characteristics. This paper aims to mine the optimal drug combination strategy from a real-life decision-making scenario of co-medication between CMM and western medicines. It abstracts this realistic problem as a class of complex network optimal overlapping community detection problem, focusing on solving key difficulties such as the inability of existing drug complex network models to handle the multi-source information of drugs and the randomness in the community detection process. We integrate co-medication prescription information with attribute information to construct the speaker-listener label propagation algorithm based on semantic attribute features (SA-SLPA) and develop a new medicine combination identification strategy based on weighted complex networks. Using 6671 clinically real prescriptions, we conclude that the detection quality of the SA-SLPA method proposed in this paper is improved by up to 78% compared to existing methods. This improvement illustrates the scientificity and effectiveness of the model in this paper and provides new ideas for the application of theoretical models and methods of interdisciplinary disciplines such as decision science, data science, and computational science in the field of modern medicine.

Integrating bipartite network modelling and overlapping community detection: A new method to evaluate transit line coordination

Integrating bipartite network modelling and overlapping community detection: A new method to evaluate transit line coordination