An Evolutionary Algorithm Based on Multi-view and Prior Information for Community Detection

Complex networks represent various real‐world systems. Overlapping community detection is one of the critical tasks in studying these networks and has significance to a wide variety of applications, including the exploration of online social networks because of the natural attitude of persons to participate in multiple communities at the same time. Despite a large number of existing community detection algorithms for detecting disjoint communities, the efficient and fast uncovering of overlapping communities has remained a challenging problem. To provide an efficient solution, on the one hand, the balanced link density label propagation (BLDLP) algorithm, proposed by the authors of the current study, is a fast, stable, and efficient method for disjoint community detection. On the other hand, the fuzzy theory is a worthwhile approach for overlapping community detection since it provides the membership rate of the overlapping nodes as well as the detection of overlapping communities. Hence, in this paper, based on the synergy of the BLDLP algorithm and the fuzzy theory, a novel method, called fuzzy BLDLP, for overlapping community detection is proposed. Fuzzy BLDLP is fast and efficient. The proposed method needs no prior information about the number of network communities to discover them. The experiments on both synthetic and real‐world known networks, including Zachary, Dolphins, and COVID‐19 Co‐authorship, have revealed that the proposed method successfully detects the overlapping nodes and communities and hence is comparable with the state‐of‐the‐art overlapping community detection algorithms in terms of recall, precision, F‐score and overlapping normalized mutual information.

Community detection methods that are based entirely on the topology of the network do not always achieve higher accuracy. This implies that the topological information alone is insufficient to accurately uncover the community structures of networks. Recently, some methods were proposed that used prior information to improve the performance and accuracy of community detection. However, most of these methods have high time consumption and are not suitable for dealing with large-scale networks. In this paper, we propose a fast semi-supervised community detection method called SemiAttractor that integrates the prior information into the distance dynamics model. Experimental results from both artificial and real-world networks show that the proposed method can effectively improve the accuracy of community detection and reduce the time costs.

Communities are virtually ubiquitous in real-world networks, and the statistic of modularity index Q is the classical measurement for community detection algorithms. However, the relationship between the modularity property and network multilever micro-scale structures is still not clear. In this paper, we study community detection results both in artificial and real-life complex networks by constructing different order null networks, and the results uncover that how micro-structures (such as degree distribution, assortativity and clustering coefficient) affect community properties. Meanwhile, we also propose two novel null networks (increasing or decreasing community structures) to verify the robustness of different community detection algorithms. Our results indicate that the modularity index Q is not a suitable statistic to measure the weak community property which is widely available in empirical networks. Our findings can not only be used to test the robustness of different community detection methods, but also be helpful to uncover the correlation of network structures between microcosmic and mesoscopic scales.

Community detection is an important research issue for understanding the structures of social networks. The existing community detection algorithms can be classified into two major classes: clustering based on modularity and spectral clustering. In this paper, we present a novel method for detecting community structures without prior information such as the number and size of communities in networks. This approach is based on a matrix called RS which enhances the cluster-properties in the data. We reveal the reason why a two-layer GCN is enough to achieve satisfied results. Our partition algorithm divides a network into different communities according to the similarity of the number of mutual neighbours among nodes, and makes use of a silhouette coefficient to determine the final unique partition. Experiments show that our community detection method can obtain accurate community structures on real-world networks, outperforming existing leading algorithms.

Research on community structures promotes the discovery of the relationship between network structure and functionality, while community detection is the foundation and core of community structure research. In this study, a community division algorithm is proposed based on a hierarchical division; a modified Jaccard similarity coeffcient is employed to detect the edges between the nodes; the network is decomposed by deleting edges between nodes to detect community structures within a network. According to the experiments on the datasets of artificial networks and real networks, this algorithm can yield accurate and meaningful community structure without prior information, of which the accuracy exceeds or approaches to that of classic community detection algorithms. In addition, compared with the classic GN splitting algorithm, the proposed algorithm produces a division of community structures that is consistent with that of GN algorithm, with a significantly improved time performance.

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.

This thesis focuses on the pressing issue of illicit drug trafficking and its impact on public health and safety at a global level. With the advent of digital technologies and social media platforms, combating drug trafficking has become increasingly challenging for law enforcement and researchers alike. Among these platforms, Instagram, a popular photo and video-sharing social networking platform, has emerged as a prominent hub for drug trafficking activities. In this study, we delve into the effectiveness of community and key player detection algorithms in identifying and disrupting illicit drug supply networks on Instagram. To conduct our research, we collected real Instagram data spanning from June to August 2022. We examined several community detection algorithms, including Louvain, Newman-Girvan, Infomap, Label Propagation, and Hierarchical Clustering. Additionally, we explored key player algorithms, namely CDKPE, TopRank, K-core, and KPEI, the latter being a novel algorithm introduced in this study. Our objective was to assess the performance of these algorithms in accurately identifying and targeting drug trafficking networks. Our findings reveal that the Louvain and Newman-Girvan algorithms outperformed others in terms of community detection, demonstrating their effectiveness in identifying cohesive groups involved in drug trafficking on Instagram. In terms of key player detection, the CDKPE and KPEI algorithms emerged as the most effective, highlighting the individuals who play pivotal roles within these networks. These algorithms offer practical applications for law enforcement agencies seeking to disrupt drug trafficking operations on Instagram. By emphasizing the importance of leveraging advanced analytical tools, our study underscores the significance of combating drug trafficking on social media platforms.

With very fast global instant messaging services, communication networks of today are changing rapidly. To accurately represent such dynamic networks, a special type of a graph, known as temporal graph is indispensable. Majority of the community detection algorithms are designed for static graphs. So, an efficient conversion of temporal graphs to static graphs, while retaining important temporal information, enables the use of any standard community detection algorithm easily. This paper proposes a novel community detection method by constructing static graphs from temporal networks. The proposed method is validated through experiments. Results show that our technique leads to considerable runtime reduction upon dealing with large graphs comprise multiple community structures.

Most of the Bayesian literature on statistical techniques in auditing has focused on assessing appropriate prior density using parameters such as interest, error rate and the mean of the error amount. Frequently, prior beliefs and mathematical tractable reasons are jointly used to assess prior distributions. As a robust Bayesian approach, we propose to replace the prior distribution with a set of prior distributions compatible with auditor's beliefs. We show how an auditor may draw the behaviour of the posterior error rate, using only partial prior information (quartiles of the prior distribution for the error rate O and, very often, the prior distribution is assumed to be unimodal). An example is pursued in depth.

Community reinforcement: An effective and efficient preprocessing method for accurate community detection

MIRACLE: A multiple independent random walks community parallel detection algorithm for big graphs

Statistical inference procedures are considered when less complete prior information is available than usually considered. For the purposes of this paper, the prior information is taken to be the specification of a set of probability measures <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\cal P</tex> . With any one prior probability measure the corresponding Bayes' estimate may be found; the recommended inference procedure when a whole set of prior probabilities <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\cal P</tex> is available is to find the whole set of estimates corresponding to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\cal P</tex> --this is called the set of feasible estimates <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\^{\Theta}</tex> . The procedure is shown to have some justification on philosophical grounds. Practical justification is also given in that finding <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">\^{\Theta}</tex> is computationally feasible in particular cases--those cases investigated here include median, minimum mean square error (MMSE), and maximum {\em a posteriori} probability (MAP) estimation.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder

PurposeThe purpose of this paper is to investigate Japanese consumer perceptions of the benefits of consuming fresh leafy vegetables.Design/methodology/approachAn online bulletin board survey was conducted in Japan to collect responses to an open-ended question about reasons for consuming fresh leafy vegetables. A total of 897 responses were analysed using word co-occurrence network analysis. A community detection method and centrality measures were used to interpret the resulting network map.FindingsUsing a community detection algorithm, the authors identify six major groups of words that represent respondents’ core motives for consuming leafy vegetables. While Japanese consumers view health benefits to be most important, sensory factors, such as texture, colour, and palatability, and convenience factors also influence attitudes. The authors find that centrality measures can be useful in identifying keywords that appear in various contexts of consumer responses.Originality/valueThis is the first paper to use a quantitative text analysis to examine consumer perceptions for fresh leafy vegetables. The analysis also provides pointers for creating visually interpretable co-occurrence network maps from textual data and discusses the role of community structure and centrality in interpreting such maps.