scholarly journals Community Detection Algorithm Based on Intelligent Calculation of Complex Network Nodes

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yanjia Tian ◽  
Xiang Feng
Keyword(s):  
Data Mining ◽  
Community Detection ◽  
Real Life ◽  
Research Direction ◽  
Detection Algorithm ◽  
The Political ◽  
Data Set ◽  
Political Blogs ◽  
Network Nodes ◽  
Community Detection Algorithm

With the explosive development of big data, information data mining technology has also been developed rapidly, and complex networks have become a hot research direction in data mining. In real life, many complex systems will use network nodes for intelligent detection. When many community detection algorithms are used, many problems have arisen, so they have to face improvement. The new detection algorithm CS-Cluster proposed in this paper is derived by using the dissimilarity of node proximity. Of course, the new algorithm proposed in this article is based on the IGC-CSM algorithm. It has made certain improvements, and CS-Cluster has been implemented in the four algorithms of IGC-CSM, SA-Cluster, W-Cluster, and S-Cluster. The result of comparing the density value on the entropy value of the Political Blogs data set, the DBLP data set, the Political Blogs data set, and the entropy value of the DBLP data set is shown. Finally, it is concluded that the CS-Cluster algorithm is the best in terms of the effect and quality of clustering, and the degree of difference in the subgraph structure of clustering.

Recent trends on community detection algorithms: A survey

2020 ◽  
Vol 34 (35) ◽  
pp. 2050408
Author(s):  
Sumit Gupta ◽  
Dhirendra Pratap Singh
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Data Sets ◽  
Detection Algorithms ◽  
Life Problems ◽  
Application Data ◽  
Art Community ◽  
Community Detection Algorithm ◽  
Two Parameters

In today’s world scenario, many of the real-life problems and application data can be represented with the help of the graphs. Nowadays technology grows day by day at a very fast rate; applications generate a vast amount of valuable data, due to which the size of their representation graphs is increased. How to get meaningful information from these data become a hot research topic. Methodical algorithms are required to extract useful information from these raw data. These unstructured graphs are not scattered in nature, but these show some relationships between their basic entities. Identifying communities based on these relationships improves the understanding of the applications represented by graphs. Community detection algorithms are one of the solutions which divide the graph into small size clusters where nodes are densely connected within the cluster and sparsely connected across. During the last decade, there are lots of algorithms proposed which can be categorized into mainly two broad categories; non-overlapping and overlapping community detection algorithm. The goal of this paper is to offer a comparative analysis of the various community detection algorithms. We bring together all the state of art community detection algorithms related to these two classes into a single article with their accessible benchmark data sets. Finally, we represent a comparison of these algorithms concerning two parameters: one is time efficiency, and the other is how accurately the communities are detected.

scholarly journals A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Dongqing Zhou ◽  
Xing Wang
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Detection Problem ◽  
Signed Networks ◽  
Discrete Pso ◽  
Community Detection Algorithm ◽  
Definition Of ◽  
The Impact

The paper addresses particle swarm optimization (PSO) into community detection problem, and an algorithm based on new label strategy is proposed. In contrast with other label propagation strategies, the main contribution of this paper is to design the definition of the impact of node and take it into use. Special initialization and update approaches based on it are designed in order to make full use of it. Experiments on synthetic and real-life networks show the effectiveness of proposed strategy. Furthermore, this strategy is extended to signed networks, and the corresponding objective function which is called modularity density is modified to be used in signed networks. Experiments on real-life networks also demonstrate that it is an efficacious way to solve community detection problem.

scholarly journals Evaluating the role of community detection in improving influence maximization heuristics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
László Hajdu ◽  
Miklós Krész ◽  
András Bóta
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Influence Maximization ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Overlapping Community ◽  
Community Detection Algorithm ◽  
The Cost

AbstractBoth community detection and influence maximization are well-researched fields of network science. Here, we investigate how several popular community detection algorithms can be used as part of a heuristic approach to influence maximization. The heuristic is based on the community value, a node-based metric defined on the outputs of overlapping community detection algorithms. This metric is used to select nodes as high influence candidates for expanding the set of influential nodes. Our aim in this paper is twofold. First, we evaluate the performance of eight frequently used overlapping community detection algorithms on this specific task to show how much improvement can be gained compared to the originally proposed method of Kempe et al. Second, selecting the community detection algorithm(s) with the best performance, we propose a variant of the influence maximization heuristic with significantly reduced runtime, at the cost of slightly reduced quality of the output. We use both artificial benchmarks and real-life networks to evaluate the performance of our approach.

scholarly journals Distributed Community Detection based on Apache Spark using Multi Label Propagation for Digital Social Networks

2018 ◽  
Vol 7 (4.5) ◽  
pp. 79
Author(s):  
Satya Keerthi Gorripati ◽  
Valli Kumari
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Apache Spark ◽  
Community Group ◽  
Community Based ◽  
Data Set ◽  
Interactive Mode ◽  
Community Detection Algorithm

Organization, Government and Individual (OGI) have popularized the use of Digital Social Networks (DSN) that reduces the processing time of social-aware tasks. To accomplish a community-based communication, each social-aware task should identify its community group. The identified group uses a task to avail all the DSN benefits to their customers / citizens. As a result, the community-based detection algorithm has played a significant role in literature. However, the existing algorithms have had several challenging issues, such as performance and scalability. Thus, a distributed community detection algorithm is presented using Apache Spark’s Resilient Distributed Data Set (RDD) framework based on the Scala programming language. The Apache Spark framework provides an ideal solution that offers ease of coding, performance, interactive mode and disk Input-Output bottlenecks in Hadoop /Map Reduce. Besides, it presents a platform of distributed community detection that reduces the computational computation by applying transformations, aggregations and joins. The experimental results show that the proposed framework achieves high accuracy for both real-world and synthetic networks.  

scholarly journals An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhixiao Wang ◽  
Ya Zhao ◽  
Zhaotong Chen ◽  
Qiang Niu
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Local Maximum ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Network Nodes ◽  
Pagerank Algorithm ◽  
Community Detection Algorithm ◽  
New Community ◽  
Almost All

Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.

scholarly journals A Feasible Community Detection Algorithm for Multilayer Networks

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 223
Author(s):  
Dongming Chen ◽  
Panpan Du ◽  
Qianrong Jiang ◽  
Xinyu Huang ◽  
Dongqi Wang
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Aggregation Model ◽  
Propagation Algorithm ◽  
Network Aggregation ◽  
Community Detection Algorithm

As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer and interlayer relationships, which is of significance and remains a challenge. In this paper, aiming at the instability of the label propagation algorithm (LPA), an improved label propagation algorithm based on the SH-index (SH-LPA) is proposed. By analyzing the characteristics and deficiencies of the H-index, the SH-index is presented as an index to evaluate the importance of nodes, and the stability of the SH-LPA algorithm is verified by a series of experiments. Afterward, considering the deficiency of the existing multilayer network aggregation model, we propose an improved multilayer network aggregation model that merges two networks into a weighted single-layer network. Finally, considering the influence of the SH-index and the weight of the edge of the weighted network, a community detection algorithm (MSH-LPA) suitable for multilayer networks is exhibited in terms of the SH-LPA algorithm, and the superiority of the mentioned algorithm is verified by experimental analysis.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

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