Community Detection In Social Networks Research Articles

Constrained Community Detection in Social Networks

Community detection in networks is the process by which unusually well-connected sub-networks are identified–a central component of many applied network analyses. The paradigm of modularity quality function optimization stipulates a partition of the network’s vertexes that maximizes the difference between the fraction of edges within communities and the corresponding expected fraction if edges were randomly allocated among all vertex pairs while conserving the degree distribution. The modularity quality function incorporates exclusively the network’s topology and has been extensively studied whereas the integration of constraints or external information on community composition has largely remained unexplored. We define a greedy, recursive-backtracking search procedure to identify the constitution of high-quality network communities that satisfy the global constraint that each community be comprised of at least one vertex among a set of so-called special vertexes and apply our methodology to identifying health care communities (HCCs) within a network of hospitals such that each HCC consists of at least one hospital wherein at least a minimum number of cardiac defibrillator surgeries were performed. This restriction permits meaningful comparisons in cardiac care among the resulting health care communities by standardizing the distribution of cardiac care across the hospital network.

Probabilistic Community Detection in Social Networks

The detection of community structures is a very crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community. More importantly, these articles are spread across a large number of different disciplines, from computer science, to statistics, and social sciences. The analysis of modern social networks becomes rather cumbersome, as their size and number keeps growing larger and larger. Moreover, in the modern communities, users participate in large number of groups. From the network perspective, efficient methods should be developed to automatically identify overlapping communities, that is, communities with overlapping nodes. In this work, we use a probabilistic network model to characterize and identify linked communities with common nodes. The innovative idea in this work is that the communities are represented as Markovian networks with continuously changing states. Each state represents the number of users within a cluster, that have specific characteristic classes. Based on the current state, we introduce a fast, linear on the number of newly added users, approach to estimate the probability of each cluster to be homogeneous in terms of sets of user characteristics and to determine how well the new user fit within a community. Because of the linear computations involved, our proposed probabilistic model can detect communities and overlaps with low execution time and high accuracy, as shown in our experimental results. The experimental results have shown that our probabilistic scheme executes faster and provides more robust communities compared to competitive schemes.

Community Detection Algorithms in Healthcare Applications: A Systematic Review

Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

Innovation evolution of industry-university-research cooperation under low-carbon development background: In case of 2 carbon neutrality technologies

Offsetting carbon emissions by various means is carbon neutrality. Technological innovation is an important path to carbon neutrality and has been framed in China’s policy planning, such as innovations about cleaner combustion technologies. To understand the current innovation of clean technology in gas boilers, this paper selects 2 Carbon Neutrality Technologies (CNT) identified in China, respectively low nitrogen combustion and catalytic combustion, to obtain relevant patent data of innovative technology. Besides, social network analysis (SNA) and community detection algorithms are used to show the evolution of three stages from 2005 to 2020. On the whole, several enterprises which dominate the network in the innovation cooperation of clean combustion technology in China exist at all stages, and the participation consciousness of scientific research institutions in universities is not strong. Network analysis shows that the network average degree fluctuates (1.172–1.962–1.442) with no obvious changes, and the network becomes sparse gradually. The analysis of the two-mode network shows that B01J refers to the most technical field involved in the cooperation except the first stage. B01D, F23G, and F23D are the key areas of technological innovation in the three stages except for B01J. The current innovations in clean combustion technology are not so much about combustion itself, instead, they focus on general physical and chemical methods. But at Stage2 and Stage3, more attention has begun to shift to burner innovation.

AC2CD: An actor–critic architecture for community detection in dynamic social networks

A vital problem tackled in the network analysis literature is community structure identification in social networks. There are many solutions to the community detection problem. Nevertheless, most of the approaches are constrained to static scenarios. Some solutions adapted to dynamic social networks present performance limitations, and others do not fit well in such contexts. This situation aggravates when considering the demand to analyze constantly growing networks as real-world social networks. This work presents the Actor–Critic for Community Detection (AC2CD) architecture for community detection in dynamic social networks. The architecture based on the deep reinforcement learning strategy deals with changing aspects of large networks using a local optimization of the modularity density function. The experiments using real-world dynamic network datasets present better results than state-of-art solutions, indicating that AC2CD copes well with dynamic real-world social networks.

An Improved Harris Hawks Optimization Algorithm with Multi-strategy for Community Detection in Social Network

An Improved Harris Hawks Optimization Algorithm with Multi-strategy for Community Detection in Social Network

Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation

The node embedding method enables network structure feature learning and representation for social network community detection. However, the traditional node embedding method only focuses on a node’s individual feature representation and ignores the global topological feature representation of the network. Traditional community detection methods cannot use the static node vector from the traditional node embedding method to calculate the dynamic features of the topological structure. In this study, an incremental dynamic community detection model based on a graph neural network node embedding representation is proposed, comprising the following aspects. A node embedding model based on influence random walk improves the information enrichment of the node feature vector representation, which improves the performance of the initial static community detection, whose results are used as the original structure of dynamic community detection. By combining a cohesion coefficient and ordinary modularity, a new modularity calculation method is proposed that uses an incremental training method to obtain node vector representation to detect a dynamic community from the perspectives of coarse- and fine-grained adjustments. A performance analysis based on two dynamic network datasets shows that the proposed method performs better than benchmark algorithms based on time complexity, community detection accuracy, and other indicators.

An enhanced multi-objective biogeography-based optimization for overlapping community detection in social networks with node attributes

Community detection is one of the most important and interesting issues in social network analysis. Most of the current community detection algorithms tend to find communities in social networks with just considering the topological structures of the networks. In recent years, simultaneously considering of nodes’ attributes and topological structures of social networks in the process of community detection has attracted the attentions of many scholars, and this consideration has been recently used in some community detection methods to increase their efficiencies and to enhance their performances in finding meaningful and relevant communities. But the problem is that most of these methods tend to find non-overlapping communities, while many real-world networks include communities that often overlap to some extent. In order to solve this problem, an evolutionary algorithm called MOBBO-OCD, which is based on multi-objective biogeography-based optimization (BBO), is proposed in this paper to automatically find overlapping communities in a social network with node attributes with synchronously considering the density of connections and the similarity of nodes’ attributes in the network. In MOBBO-OCD, an extended locus-based adjacency representation called OLAR is introduced to encode and decode overlapping communities. Based on OLAR, a rank-based migration operator along with a novel two-phase mutation strategy and a new double-point crossover are used in the evolution process of MOBBO-OCD to effectively lead the population into the evolution path. In order to assess the performance of MOBBO-OCD, a new metric called alpha_SAEM is proposed in this paper, which is able to evaluate the goodness of both overlapping and non-overlapping partitions with considering the two aspects of node attributes and linkage structure. Quantitative evaluations, based on three extensive experiments on 14 real-life data sets with diverse characteristics, reveal that MOBBO-OCD achieves favorable results which are quite superior to the results of 15 relevant community detection algorithms in the literature.

GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection.

Community detection in semantic social networks is a crucial issue in online social network analysis, and has received extensive attention from researchers in various fields. Different conventional methods discover semantic communities based merely on users’ preferences towards global topics, ignoring the influence of topics themselves and the impact of topic propagation in community detection. To better cope with such situations, we propose a Gaming-based Topic Influence Percolation model (GTIP) for semantic overlapping community detection. In our approach, community formation is modeled as a seed expansion process. The seeds are individuals holding high influence topics and the expansion is modeled as a modified percolation process. We use the concept of payoff in game theory to decide whether to allow neighbors to accept the passed topics, which is more in line with the real social environment. We compare GTIP with four traditional (GN, FN, LFM, COPRA) and seven representative (CUT, TURCM, LCTA, ACQ, DEEP, BTLSC, SCE) semantic community detection methods. The results show that our method is closer to ground truth in synthetic networks and has a higher semantic modularity in real networks.

Community Detection in Semantic Networks: A Multi-View Approach.

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.

How to Protect Ourselves From Overlapping Community Detection in Social Networks

In recent years, overlapping community detection algorithms have been paid more and more attention, which not only reveal the real social relations, but also expose the possible communication channels between communities. Those individuals (or people) in the overlapping area are very important to the communities that can promote communication between two or more communities. On the other hand, from the privacy perspective, some people may not want to be found out in the overlapping areas. With this in mind, we raise a question “Can individuals modify their relationships to avoid the community discovery algorithms locating them into overlapping areas?” If this problem could be solved, these people may not need to worry about being disturbed. In particular, we first give three heuristic hiding strategies, i.e., Random Hiding(RH), Based Degree Hiding(DH) and Betweenness Hiding(BH), as comparison, utilizing the randomly the node, information of node degree and node betweenness centrality, respectively. And then, we propose a novel hiding algorithm by exploiting the importance degree of nodes in communities based on which the corresponding social connections are added or deleted called name <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BIH</i> . Through extensive experiments, we show the effectiveness of the proposed algorithm in moving out a target node from overlapped areas.

Densest subgraph discovery on large graphs

As one of the most fundamental problems in graph data mining, the densest subgraph discovery (DSD) problem has found a broad spectrum of real applications, such as social network community detection, graph index construction, regulatory motif discovery in DNA, fake follower detection, and so on. Theoretically, DSD closely relates to other fundamental graph problems, such as network flow and bipartite matching. Triggered by these applications and connections, DSD has garnered much attention from the database, data mining, theory, and network communities. In this tutorial, we first highlight the importance of DSD in various applications and the unique challenges that need to be addressed. Subsequently, we classify existing DSD solutions into several groups, which cover around 50 research papers published in many well-known venues (e.g., SIGMOD, PVLDB, TODS, WWW), and conduct a thorough review of these solutions in each group. Afterwards, we analyze and compare the models and solutions in these works. Finally, we point out a list of promising future research directions. We believe that this tutorial not only helps researchers have a better understanding of existing densest subgraph models and solutions, but also provides them insights for future study.

Link Pruning for Community Detection in Social Networks

Attempts to discover knowledge through data are gradually becoming diversified to understand complex aspects of social phenomena. Graph data analysis, which models and analyzes complex data as graphs, draws much attention as it combines the latest machine learning techniques. In this paper, we propose a new framework called link pruning for detecting clusters in complex networks, which leverages the cohesiveness of local structures by removing unimportant connections. Link pruning is a flexible framework that reduces the clustering problem in a highly mixed community structure to a simpler problem with a lowly mixed community structure. We analyze which similarities and curvatures defined on the pairs of nodes, which we call the link attributes, allow links inside and outside the community to have a different range of values. Using the link attributes, we design and analyze an algorithm that eliminates links with low attribute values to find a better community structure on the transformed graph with low mixing. Through extensive experiments, we have shown that clustering algorithms with link pruning achieve higher quality than existing algorithms in both synthetic and real-world social networks.

A novel attributed community detection by integration of feature weighting and node centrality

Community detection is one of the primary problems in social network analysis and this problem has more challenges in attributed social networks. The purpose of community detection in attributed social networks is to discover communities with not only homogeneous node properties but also adherent structures. Although community detection has been extensively studied, attributed community detection of large social networks with a large number of attributes remains a vital challenge. To address this challenge, in this paper a novel attributed community detection method is developed by integration of feature weighting with node centrality techniques. The developed method includes two main phases: (1) Weight Matrix Calculation, (2) Label Propagation Algorithm-based Attributed Community Detection. The aim of the first phase is to calculate the weight between two linked nodes using structural and attribute similarities, while, in the second phase, an improved label propagation algorithm-based community detection method in the attributed social network is proposed. The purpose of the second phase is to detect different communities by employing the calculated weight matrix and node popularity. After implementing the proposed method, its performance is compared with several other state of the art methods using some benchmarked real-world datasets. The results indicate that the developed method outperforms several other state-of-the-art methods and ascertain the effectiveness of the developed method for attributed community detection.

SSPCO: A new particle propagation model for community detection in complex network

Community detection is of extraordinary significance in comprehending the structure and functions of complex networks. The particle competition algorithm is a quick and heuristic algorithm when applied to community detection. However, existing particle competition algorithms do not make full use of all information of networks and have several shortcomings such as poor robustness, weak stability, and low accuracy. In addition, it cannot be effectively applied to overlapping community detection. In this paper, a new particle propagation model with semi-supervised learning for community detection in social networks (SSPCO) is proposed. SSPCO divides the formation process of communities into the initialization phase, walking phase, restart phase, convergence phase and overlapping community detection phase. In the initialization phase, each team of labeled vertices generates a particle of this team. The domination level of each team particles at vertices and edges is also initialized in this phase. In the walking phase, the particle walks to one of the neighbors of the current vertex based on the proposed walking probability calculated by the proposed transfer acceptance probability and the proposed transfer proposal probability. In the process of particle walking, the particle has a possibility of entering the restart phase. The proposed restart probability determines whether the particle performs the restart mechanism. If the particle decides to restart, it will select a vertex for restart based on the domination level of this team particles at vertices. Otherwise, it continues to walk. After several particle walking and restart phases, the particle meets the convergence state. In the convergence phase, if all particles meet the proposed convergence condition, we will obtain community partition results based on the domination level of each team particles at the vertex. In the overlapping community detection phase, we can obtain overlapping community partition results based on the overlapping community detection mechanism. Experiment results reveal that SSPCO can improve the stability and accuracy of detecting communities. Moreover, SSPCO runs in near-linear time complexity, which allows it to be applied in large-sized networks.

Modified Data Delivery Strategy Based on Stochastic Block Model and Community Detection in Opportunistic Social Networks

The nodes in the opportunistic network make up communities according to the relevance between them. Some of the structural characteristics of an opportunistic network can be reflected by the structure of the communities that exist in the network. Therefore, finding community from the network is of great significance for people to better study, use, and transform the network. The overlap of communities is considered to be an important feature of communities. Almost all community discovery algorithms were based on nonoverlapping communities in the past. A node in a nonoverlapping community belongs to only one community. However, there are overlapping and interrelated characteristics between communities, so it is not in line with the actual environment of the network. As a result, the previous algorithms have many shortcomings in the face of practical application scenarios, coupled with the limitation of the computing capacity of mobile devices; data transmission for low delay and the low energy consumption is difficult to meet the requirements. In the study, we formulate the problem of dividing nodes into several communities in the opportunistic social network as how to build communities dynamically according to weight distribution. Then, we propose a modified data delivery strategy based on stochastic block model and community detection (DDBSC). The simulation results show that, compared with other algorithms in the experiments, the strategy proposed in this paper exhibits good performance in terms of overhead, energy consumption, and delivery rate.

Evaluating Methods for Efficient Community Detection in Social Networks

Exploring a community is an important aspect of social network analysis because it can be seen as a crucial way to decompose specific graphs into smaller graphs based on interactions between users. The process of discovering common features between groups of users, entitled “community detection”, is a fundamental feature for social network analysis, wherein the vertices represent the users and the edges their relationships. Our study focuses on identifying such phenomena on the Twitter graph of posts and on determining communities, which contain users with similar features. This paper presents the evaluation of six established community-discovery algorithms, namely Breadth-First Search, CNM, Louvain, MaxToMin, Newman–Girvan and Propinquity Dynamics, in terms of four widely used graphs and a collection of data fetched from Twitter about man-made and physical data. Furthermore, the size of each community, expressed as a percentage of the total number of vertices, is identified for the six particular algorithms, and corresponding results are extracted. In terms of user-based evaluation, we indicated to some students the communities that were extracted by every algorithm, with a corresponding user and their tweets in the grouping and considered three different alternatives for the extracted communities: “dense community”, “sparse community” and “in-between”. Our findings suggest that the community-detection algorithms can assist in identifying dense group of users.

A Novel Approach for Tenuous Community Detection in Social Networks

Tenuous community detection in social networks is becoming an interesting area of research and its various important applications are emerging. Existing research has focused on the identification of a dense community. However, finding the tenuous community has posed a different kinds of challenges and existing solutions are not viable for this research problem. In this paper, a novel approach called the least linked community (LLC) is proposed to find a tenuous community from online social networks. The concept of k-links with the shortest path distance between two nodes is proposed and utilized to find a community with the least interaction and weak relationships.The experiment demonstrated significant results in terms of accuracy, effectiveness, and efficiency compared to other existing techniques. Although the proposed algorithm presents significant results it may require further evaluation with different data sets.

Prediction of link evolution using community detection in social network

Prediction of link evolution using community detection in social network

CGraM: Enhanced Algorithm for Community Detection in Social Networks

Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, community detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks – CGraM, for community detection using the graph measures eccentricity, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a preliminary community structure is formed by finding the similar nodes using the jaccard coefficient. Later communities are selected from the preliminary community structure based on the number of inter-community and intra-community edges between them. Then the selected communities are merged till the modularity improves to form the better resultant community structure. This method is tested on the real networks and the results are evaluated using the evaluation metrics modularity and Normalized Mutual Information (NMI). The results are visualized and also compared with the state-of-the-art algorithms that covers louvian, walktrap, infomap, label propagation, fast greedy and eigen vector for more accurate analysis. CGraM achieved the better modularity and improved NMI values comparatively with other algorithms and gives improved results collaboratively when compared to previous methods.