scholarly journals Effectively Detecting Communities by Adjusting Initial Structure via Cores

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20
Author(s):  
Mei Chen ◽  
Zhichong Yang ◽  
Xiaofang Wen ◽  
Mingwei Leng ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Linear Time ◽  
Local Density ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Initial Structure ◽  
Community Structures ◽  
Art Community ◽  
Community Detection Algorithm

Community detection is helpful to understand useful information in real-world networks by uncovering their natural structures. In this paper, we propose a simple but effective community detection algorithm, called ACC, which needs no heuristic search but has near-linear time complexity. ACC defines a novel similarity which is different from most common similarity definitions by considering not only common neighbors of two adjacent nodes but also their mutual exclusive degree. According to this similarity, ACC groups nodes together to obtain the initial community structure in the first step. In the second step, ACC adjusts the initial community structure according to cores discovered through a new local density which is defined as the influence of a node on its neighbors. The third step expands communities to yield the final community structure. To comprehensively demonstrate the performance of ACC, we compare it with seven representative state-of-the-art community detection algorithms, on small size networks with ground-truth community structures and relatively big-size networks without ground-truth community structures. Experimental results show that ACC outperforms the seven compared algorithms in most cases.

Node Trust: an effective method to detect non-overlapping community in social networks

2020 ◽  
pp. 2150036
Author(s):  
Jinfang Sheng ◽  
Qiong Li ◽  
Bin Wang ◽  
Wanghao Guan ◽  
Jinying Dai ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Community Detection ◽  
Linear Time ◽  
Deep Understanding ◽  
Detection Algorithm ◽  
Underlying Structure ◽  
Community Detection Algorithm ◽  
Stable Community ◽  
Synthetic Networks

Social networks are made up of members in society and the social relationships established by the interaction between members. Community structure is an essential attribute of social networks. The question arises that how can we discover the community structure in the network to gain a deep understanding of its underlying structure and mine information from it? In this paper, we introduce a novel community detection algorithm NTCD (Community Detection based on Node Trust). This is a stable community detection algorithm that does not require any parameters settings and has nearly linear time complexity. NTCD determines the community ownership of a node by studying the relationship between the node and its neighbor communities. This relationship is called Node Trust, representing the possibility that the node is in the current community. Node Trust is also a quality function, which is used for community detection by seeking maximum. Experiments on real and synthetic networks show that our algorithm has high accuracy in most data sets and stable community division results. Additionally, through experiments on different types of synthetic networks, we can conclude that our algorithm has good robustness.

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.

A Novel Overlapping Community Detection Algorithm of Weighted Networks

2014 ◽  
Vol 599-601 ◽  
pp. 1369-1373
Author(s):  
Huang Bin You ◽  
Xue Wu Zhang ◽  
Huai Yong Fu ◽  
Zhuo Zhang ◽  
Min Li ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Classification Accuracy ◽  
Time Complexity ◽  
Maximum Degree ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Weighted Networks ◽  
Overlapping Community ◽  
Community Detection Algorithm

The community structure is a vital property of complex networks. As special networks the weighted networks also have community structure. Nowadays the studies of overlapping community draw attentions of researchers. However, the scale of networks become huge, so it requires the algorithm has lower time complexity and higher classification accuracy. Many existing algorithms cannot meet these two requirements at the same time. So we propose a novel overlapping community detection algorithm. Firstly we apply maximum degree node and its some special adjacent nodes as the initial community, and then expand the initial community by adding eligible nodes to it, finally other communities can be found by repeating these two steps. Experiments results show that our algorithm can detect overlapping community structure from weighted networks successfully, and also reveal that our method has higher division accuracy and lower time complexity than many previously proposed methods.

scholarly journals Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 111 ◽  
Author(s):  
Weiqin Ying ◽  
Hassan Jalil ◽  
Bingshen Wu ◽  
Yu Wu ◽  
Zhenyu Ying ◽  
...  
Keyword(s):  
Community Detection ◽  
Migration Policy ◽  
Detection Algorithm ◽  
Island Model ◽  
Multi Objective Optimization ◽  
Community Structures ◽  
Multi Objective ◽  
Local Search Strategy ◽  
Community Detection Algorithm ◽  
Global Selection

Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In addition, a modularity-based greedy local search strategy is also applied to accelerate the convergence rate. Comparative experimental results on six real-world networks reveal that PCACD is capable of discovering potential high-quality community structures at diverse resolutions with satisfactory running efficiencies.

Gravitational community detection by predicting diameter

2021 ◽  
pp. 2150145
Author(s):  
Himansu Sekhar Pattanayak ◽  
Harsh K. Verma ◽  
Amrit Lal Sangal
Keyword(s):  
Community Detection ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Local Information ◽  
Ground Truth Data ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
Np Hard Problem ◽  
Community Detection Algorithm ◽  
The Individual

Community detection is a pivotal part of network analysis and is classified as an NP-hard problem. In this paper, a novel community detection algorithm is proposed, which probabilistically predicts communities’ diameter using the local information of random seed nodes. The gravitation method is then applied to discover communities surrounding the seed nodes. The individual communities are combined to get the community structure of the whole network. The proposed algorithm, named as Local Gravitational community detection algorithm (LGCDA), can also work with overlapping communities. LGCDA algorithm is evaluated based on quality metrics and ground-truth data by comparing it with some of the widely used community detection algorithms using synthetic and real-world networks.

Active semisupervised community detection based on asymmetric similarity measure

2015 ◽  
Vol 29 (13) ◽  
pp. 1550078 ◽  
Author(s):  
Mingwei Leng ◽  
Liang Huang ◽  
Longjie Li ◽  
Hanhai Zhou ◽  
Jianjun Cheng ◽  
...  
Keyword(s):  
Community Structure ◽  
Prior Knowledge ◽  
Complex Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Directed Network ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Similarity Method ◽  
Asymmetric Similarity

Semisupervised community detection algorithms use prior knowledge to improve the performance of discovering the community structure of a complex network. However, getting those prior knowledge is quite expensive and time consuming in many real-world applications. This paper proposes an active semisupervised community detection algorithm based on the similarities between nodes. First, it transforms a given complex network into a weighted directed network based on the proposed asymmetric similarity method, some informative nodes are selected to be the labeled nodes by using an active mechanism. Second, the proposed algorithm discovers the community structure of a complex network by propagating the community labels of labeled nodes to their neighbors based on the similarity between a node and a community. Finally, the performance of the proposed algorithm is evaluated with three real networks and one synthetic network and the experimental results show that the proposed method has a better performance compared with some other community detection algorithms.

A Community Detection Algorithm Based on Node Degree Difference and Node Similarity

2013 ◽  
Vol 462-463 ◽  
pp. 458-461
Author(s):  
Jian Jun Cheng ◽  
Peng Fei Wang ◽  
Qi Bin Zhang ◽  
Zheng Quan Zhang ◽  
Ming Wei Leng ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Detection Algorithm ◽  
Coarse Grained ◽  
Node Degree ◽  
Detection Algorithms ◽  
Starting Point ◽  
Node Similarity ◽  
Feasible Algorithm ◽  
Community Detection Algorithm

This paper proposes an algorithm called DDSCDA, which is based on the concepts of the node degree difference and the node similarity. In the algorithm, we iteratively extract the node from the network with larger degree and certified the node as a kernel node, then take the kernel node as the founder or initiator of a community to attract its neighbors to join in that community; by doing so, we obtain a partition corresponding to a coarse-grained community structure of the network. Finally taken the coarse-grained community as a starting point, we use the strategy of LPA to propagate labels through the network further. At the end of the algorithm, we obtain the final community structure. We compared the performance with classical community detection algorithms such as LPA, LPAm, FastQ, etc., the experimental results have manifested that our proposal is a feasible algorithm, can extract higher quality communities from the network, and outperforms the previous algorithms significantly.

Research on Fast Community Detection Algorithm in Microblog

2014 ◽  
Vol 513-517 ◽  
pp. 2045-2049
Author(s):  
Jie Tian ◽  
Hao Guo ◽  
Yu Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Detection Method ◽  
Heuristic Method ◽  
Computation Time ◽  
Detection Algorithm ◽  
Core Structure ◽  
Large Networks ◽  
The Core ◽  
Community Detection Algorithm

According to the problem of extracting the community structure of large networks, we propose a simple heuristic method based on community coding optimization. It is shown to outperform the InfoMap community detection method in terms of computation time. Experiments show that our method can find out various communities in microblog, which reveal the core structure of the network.

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