Overlapping Community Detection Algorithm 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.

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.

Retracted: Application Analysis of Overlapping Community Detection Algorithms for Multidimensional Network Big Data and IoT

Retracted: Application Analysis of Overlapping Community Detection Algorithms for Multidimensional Network Big Data and IoT

CDRKD: An improved density peak algorithm based on kernel fuzzy measure in the overlapping community detection

Compared with other traditional community discovery algorithms, density peak clustering algorithm is more efficient in getting network structures through clustering. However, DPC needs to contain the distance information between all nodes as sources, so it cannot directly processing the complex network represented by the adjacency matrix. DPC introduces truncation distance when calculating the local density of nodes, which is usually set as a fixed value according to experience, and lacks self-adaptability for different network structures. A feasible solution to those problems is to combined rough set theory and kernel fuzzy similarity measures. In this work, we present overlapping community detection algorithm based on improved rough entropy fusion density peak. The algorithm applied rough set theory to attribute reduction of massive high-dimensional data. Another algorithm defines the similarity of sample points by the inner product between two vectors on the basis of fuzzy partition matrix. Finally, a community detection algorithm based on rough entropy and kernel fuzzy density peaks clustering (CDRKD) has proposed by combining the two algorithms above, we perform an extensive set of experiments to verify the effectiveness and feasibility of the algorithm.

Machine learning to set hyperparameters for overlapping community detection algorithms

AbstractThe Local Fitness Method (LFM) and Speaker‐Listener Label Propagation (SLPA) algorithms are widely used to detect overlapping communities in complex networks. The main problem with these two algorithms is that they are extremely sensitive to the setting of the hyperparameters. Previous methods set the hyperparameters of LFM or SLPA based on either empirical values or random choices, resulting in a large amount of computation for adjusting those parameters. To solve this problem, in this paper, a machine‐learning‐based approach is proposed to automatically set the hyperparameters of these two algorithms. Experimental results show that compared with the manual method, automatically setting the hyperparameter using machine learning models can lead to higher‐quality divisions. Furthermore, in comparison to the well‐known hyperparameter tuning method using Bayesian Optimization, the proposed predictive model‐based approach can find suitable para meters for LFM and SLPA much faster, while achieving competitive results in terms of division quality.

Multi-objective Optimization Overlapping Community Detection Algorithm based on Subgraph Structure

Community detection in complex networks has increasingly become an important topic in the network, but in most community detection methods, a single node only belongs to one community. In fact, there is often overlap among real-world online communities. In this paper, an overlapping community detection algorithm based on subgraph structure and multi-optimization method is designed. In this algorithm, the maximum clique mined by k-core decomposition is used as the clique node, thus the overlap characteristic is transformed into the inherent characteristic of the new graph. After that, a population initialization method based on k-core decomposition is designed, and the discrete framework of multi-objective particle swarm optimization algorithm is used to optimize the two objectives on the basis of maximal clique graph to solve the problem of overlapping community detection. In the real-world network, this algorithm is compared with similar community detection algorithms. The comparison of the evaluation indexe shows that the community detection effect of this algorithm is similar to that of similar algorithms, and has a good application prospect.

Overlapping community detection algorithm based on similarity of node relationship

Community discovery is a vital link in the research of social networks aiming at the shortcomings of the current local extension-based community discovery algorithm in local community discovery and extension. In this paper, we proposed a algorithm based on relationship similarity and local extension overlapping community detection (RSLO). First, use the node's relationship similarity strategy to find close seed communities. Then, according to the discovered seed community, the similarity between the neighboring nodes of the community and the community is calculated, and the nodes whose similarity meets the threshold are selected. After that, an adaptive optimization function is used to expand the community. Finally, the free nodes that have not been divided into the community are divided into communities, thereby achieving a more comprehensive community discovery. We conduct experiments on classic datasets and artificially generated networks. The results show that the RSLO algorithm can find accurate and objective community structures.

An Influence-Based Label Propagation Algorithm for Overlapping Community Detection

Of the various characteristics of network structure, the community structure has received the most research attention. In social networks, communities are divided into overlapping communities and disjoint communities. The former are closer to the actual situation of real society than the latter, making it necessary to explore a more effective overlapping community detection algorithm. The label propagation algorithm (LPA) has been widely used in large-scale data owing to its low time cost. In the traditional LPA, all of the nodes are regarded as equivalent relationships. In this case, unreliable nodes reduce the accuracy of label propagation. To solve this problem, we propose the influence-based community overlap propagation algorithm (INF-COPRA) for ranking the influence of nodes and labels. To control the propagation process and prevent error propagation, the algorithm only provides influential nodes with labels in the initialization phase, and those labels with high influence are preferred in the propagation process. Lastly, the accuracy of INF-COPRA and existing algorithms is compared on benchmark networks and real networks. The experimental results show that the INF-COPRA algorithm significantly improves the extentded modularity (EQ) and normal mutual information (NMI) of the community, indicating that it can outperform state-of-art methods in overlapping community detection tasks.

Overlapping Community Detection Algorithm Based on High-Quality Subgraph Extension in Local Core Regions of Network

Community structure is an important feature of complex networks. Detecting overlapping communities in complex networks is a hot research topic in data mining and graph theory, aiming at the shortcomings of community detection algorithm based on seed expansion, such as the instability of community detection results caused by randomly selecting seeds, the similarity of selected seeds leading to similar communities after different seed expansion, and the increase of calculation caused by deleting nodes in the process of seed expansion. This paper proposes an overlapping community detection algorithm based on high-quality subgraph extension in local core regions of the network (OLCRE). First, a novel seed community selection method is designed. By analyzing the sum of node degrees of the subgraph formed by a node and its neighbor nodes in the local core region of the network and the tightness of the internal and external connections of the subgraph, a seed community selection function is proposed. According to this function, high-quality subgraphs are selected from all the local core regions of the network as seed communities. Then, taking the definition of community as the guideline, a new community expansion strategy is proposed. Considering the influence of the neighbor node on the inner and outer connection tightness of the seed community comprehensively, it is determined whether the neighbor node can join the seed community. Finally, after the completion of all seed community expansion, overlapping nodes and possible missing nodes should be simplified and redetected to further improve the quality of community detection. The proposed algorithm is tested on the artificial and real-world networks and compared with several overlapping community detection algorithms. The experimental results verify the effectiveness and feasibility of the proposed algorithm.

A Novel Overlapping Community Detection Algorithm Combing Interest Topic and Local Density

Ontology user portraits describe the semantic structure of users’ interests. It is very important to study the similar relationship between user portraits to find the communities with overlapping interests. The hierarchical characteristics of user interest can generate multiple similarity relations, which is conducive to the formation of interest clusters. This paper proposed a method of overlapping community detection combining the hierarchical characteristics of user interest and the module distribution entropy of node. First, a hierarchical user interest model was constructed based on the ontology knowledge base to measure the multi-granularity topic similarity of users. Then, a heterogeneous hypergraph was established by using the multi-granularity topic similarity and the following similarity of users to represent the interest network. Based on the mechanism of module distribution entropy of nodes, the community detection algorithm was applied to identify the interested community. The real performance of the proposed algorithm on multiple networks was verified by experiments. The experimental results show that the proposed algorithm is better than the typical overlapping community detection algorithm in terms of accuracy and recall rate.

Parallel Overlapping Community Detection Algorithm on GPU

Community detection is one of the most representative graph mining applications, which is often assembled as a concurrent graph partition application to explore the maximum modularity (or gained modularity) of each community. However, many branch divergence operations create significant obstacles to unleashing GPU&#x2019;s high throughput and memory bandwidth, which are needed in community detection applications to divide the vertices into different communities. In this paper, we present Lugger, a GPU-based overlapping community detection algorithm that reduces GPU&#x2019;s branch divergence via the customer-designed cache-aware parallel searching technique. In Lugger, we first design a cache-aware parallel searching policy using the B-Tree structure. Then, we set the B-Tree node matches with the GPU cache line to meet the coalesced memory access manner and avoid the branch divergence in warps. Moreover, we design a positive node splitting scheme to reduce the lock operation and idle threads when building the B-Tree structure. In addition, we implement a warp-centric thread assignment strategy to make sure the workloads across threads are balanced. We implement the proposed algorithm on NVIDIA GPU and evaluate the performance on eight large graphs (up to <inline-formula><tex-math notation="LaTeX">$3M$</tex-math></inline-formula> vertices and <inline-formula><tex-math notation="LaTeX">$117M$</tex-math></inline-formula> edges) with ground-truth communities. The experimental results show that Lugger can outperform the state-of-the-art works on scalability and detection quality.

ISOCD: Overlapping community detection algorithm based on local influence and similarity

Many nodes of a complex network in the real world belong to multiple communities. Research on the detection of overlapping communities with common nodes can help us understand the topology of network and extract information hidden in the network. Considering the problems of many existing overlapping community detection methods, such as high time complexity or poor division results, we propose an overlapping community detection method based on local influence and similarity (ISOCD). The ISOCD method selects seed nodes based on local influence, follows a greedy strategy, takes the fitness function of similarity as the optimization goal, and finally optimizes and merges the detected communities. In experiments, the method we propose has good community division effects both on the real-world and synthetic networks, and has low time complexity.

An Overlapping Community Detection Approach Based on Deepwalk and Improved Label Propagation

Label propagation-based overlapping community detection algorithms have been widely used in complex networks due to their simplicity and efficiency. However, such algorithms need to randomly choose neighbor nodes and do not fully take the network’s topology into consideration, resulting in low stability and accuracy. Aiming at this problem, we propose an overlapping community detection approach based on DeepWalk and the improved label propagation. We first use the DeepWalk model to learn the network’s topology to obtain low-dimensional vector representations that reflect the spatial location of nodes and construct the weight matrix through vector dot product operation. Then, we design a label propagation algorithm with a preference selection strategy, which can obtain stable overlapping communities by exchanging information with fixed neighbors on the basis of preserving the nodes’ own labels. The experimental results on the real network and synthetic datasets show that the proposed approach has better accuracy and stability than the baseline methods.

Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networks

AbstractInfluence maximization, whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network, has many applications such as goods advertising and rumour suppression. Among the existing influence maximization methods, the community‐based ones can achieve a good balance between effectiveness and efficiency. However, this kind of algorithm usually utilise the network community structures by viewing each node as a non‐overlapping node. In fact, many nodes in social networks are overlapping ones, which play more important role in influence spreading. To this end, an overlapping community‐based particle swarm optimization algorithm named OCPSO for influence maximization in social networks, which can make full use of overlapping nodes, non‐overlapping nodes, and their interactive information is proposed. Specifically, an overlapping community detection algorithm is used to obtain the information of overlapping community structures, based on which three novel evolutionary strategies, such as initialisation, mutation, and local search are designed in OCPSO for better finding influential nodes. Experimental results in terms of influence spread and running time on nine real‐world social networks demonstrate that the proposed OCPSO is competitive and promising comparing to several state‐of‐the‐arts (e.g. CGA, CMA‐IM, CIM, CDH‐SHRINK, CNCG, and CFIN).

Multi-objective optimisation model under multiplex weighted drivers’ collaboration network: Risk, time and profit management perspectives

This study aims to devise a multi-objective optimisation model through the use of drivers’ information diffusion capacity in transportation service procurement [TSP]. In this model, in addition to the profit optimisation of transportation companies, the allocation of freights to the most suitable drivers in the shortest possible time is considered. First, driver-to-driver interactions are modelled on a weighted drivers’ collaboration network and an overlapping community detection algorithm is used to identify the communities’ leaders and their drivers. Also, an objective function is developed to optimise the speed of order allocation to drivers in communities. To reduce the risk of losing customers, the VIKOR method is developed for ranking and allocating shipping orders to the most qualified drivers. This optimisation model enables us to simultaneously manage the three factors of time, cost, and suitability of drivers in allocating shipping orders to drivers. By solving this model, the results show novice companies tend to allocate orders to drivers in the shortest time, but well-known companies are willing to assign the orders with the lowest cost. Therefore, the results reflect the real behaviour of the TSP system, and companies can adopt policies corresponding to their risk level in allocating orders to drivers.

A Temporal and Social Network-based Recommender using Graph Clustering

Recommendation Systems (RSs) have significant applications in many industrial systems. The duty of a recommender algorithm is to operate available data (users/items contextual data and rating (or purchase) the consumption history for items), as well as to provide a recommendation list for any target user. The recommended items should be selected so that the target user is compelled to give them positive reviews. In this manuscript, we propose a novel of RS algorithm that makes advantage of user-user trust relationships, rating histories, and their frequency of occurrence. We also provide a brand new overlapping community detection algorithm. The information about the users’ community structure is used to handle the cold-start and sparsity problems. We compare the performance of the proposed RS algorithm with a number of state-of-the-art algorithms on the extended Epinions dataset, which has both information on trust relations and the timing of the ratings. Numerical simulations reveal the superiority of the proposed algorithm over others. We also investigate how the algorithms perform when only cold-start users and items are considered. As a cold-start user (item) we consider those that have made (received) less than five ratings. The experiments show significant outperformance of the proposed algorithm over others, which is mainly due to the use of information on overlapping community structures between users.

Overlapping Community Hiding Method Based on Multi-Level Neighborhood Information

The overlapping community detection algorithm divides social networks into multiple overlapping parts, and members can belong to multiple communities at the same time. Although the overlapping community detection algorithm can help people understand network topology, it exposes personal privacy. The BIH algorithm is proposed to solve the problem of personal privacy leaks in overlapping areas. However, some specific members in overlapping areas do not want to be discovered to belong to some specific community. To solve this problem, an overlapping community hiding algorithm based on multi level neighborhood information (MLNI) is proposed. The MLNI algorithm defines node probability of community based on multi-layer neighborhood information. By adjusting the probability of the target node belonging to each community, the difference between the probability that the target node belongs to outside and inside the target community is maximized. This process can be regarded as an optimization problem. In addition, the MLNI algorithm uses the genetic algorithm to find the optimal solution, and finally achieves the purpose of moving the target node in the overlapping area out of a specific community. The effectiveness of the MLNI algorithm is demonstrated through extensive experiments and baseline algorithms. The MLNI algorithm effectively realizes the protection of personal privacy in social networks.

A Supervised Link Prediction Method Using Optimized Vertex Collocation Profile

Classical link prediction methods mainly utilize vertex information and topological structure to predict missing links in networks. However, accessing vertex information in real-world networks, such as social networks, is still challenging. Moreover, link prediction methods based on topological structure are usually heuristic, and mainly consider common neighbors, vertex degrees and paths, which cannot fully represent the topology context. In recent years, network embedding models have shown efficiency for link prediction, but they lack interpretability. To address these issues, this paper proposes a novel link prediction method based on an optimized vertex collocation profile (OVCP). First, the 7-subgraph topology was proposed to represent the topology context of vertexes. Second, any 7-subgraph can be converted into a unique address by OVCP, and then we obtained the interpretable feature vectors of vertexes. Third, the classification model with OVCP features was used to predict links, and the overlapping community detection algorithm was employed to divide a network into multiple small communities, which can greatly reduce the complexity of our method. Experimental results demonstrate that the proposed method can achieve a promising performance compared with traditional link prediction methods, and has better interpretability than network-embedding-based methods.

A subgraphs-density based overlapping community detection algorithm for large-scale complex networks

A subgraphs-density based overlapping community detection algorithm for large-scale complex networks

Application Analysis of Overlapping Community Detection Algorithms for Multidimensional Network Big Data and IoT

Capture the design and elements of these layers. Each layer corresponds to an alternative connection type between hubs in the normal world and requires tracking down communities in multidimensional networks. Most community disclosure approaches for multidimensional networks, then again, may ignore the transaction between layers or a layer’s unmistakable topological construction. Moreover, most of them are just equipped for distinguishing nonoverlapping communities. In this exploration, we offer another multidimensional network community disclosure strategy that exploits the connection among layers and the extraordinary geography of each layer to track down overlapping communities. First, use an overall assessment of edge behavior within and between layers to calculate the similarity of edges from similar layers and cross layers. You can then use these similarities to build a dendrogram of a multidimensional network that takes into account both characteristic topology structures and basic transactions. Finally, you can remove the overlapping communities in these layers by splitting the dendrogram and adding another community thickness metric for the multidimensional network. We show that our strategy is precise in recognizing overlapping communities in multidimensional networks by applying it to both manufactured and genuine world datasets. In chart and enormous information examination, community detection is a commonplace issue. It involves finding groups of firmly associated hubs with little associations with hubs outside the bunch. Distinguishing communities in huge scope networks, specifically, is a basic errand in numerous logical fields. In the writing, community detection techniques have been demonstrated to be wasteful, bringing about the improvement of communities with uproarious communications. To defeat this requirement, a framework that decides the best community among multifaceted networks in light of important determination standards and substance dimensionality should be created, eliminating loud communications in a continuous setting. Our outcomes likewise show that it is vital to utilize integral measurements to assess the exhibition of overlapping community detection calculations. Performance metrics, such as the NMI or the Omega Index, only measure the overall quality of a detected cover, whereas complementary metrics give us more information about the behavior of each algorithm in detecting overlapping communities. Finally, while some algorithms perform well on synthetic networks, none of the algorithms can detect the community structure in real networks. This is due to the detected communities of the algorithms being substantially different from the communities defined by the meta-data.