scholarly journals A New Algorithm and Its Application in Detecting Community of the Bipartite Complex Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhongyi Lei ◽  
Haiying Wang
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Time Complexity ◽  
Suffix Tree ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Overlapping Communities ◽  
Adjacent Node ◽  
The Matrix ◽  
Community Mining

The community division of bipartite networks is one frontier problem on the research of complex networks today. In this study, we propose a model of community detection of the bipartite network, which is based on the generalized suffix tree algorithm. First, extract the adjacent node sequences from the matrix of relation and use the obtained adjacent node sequences to build a generalized suffix tree; second, traverse the established generalized suffix tree to obtain the bipartite cliques; third, adjust the bipartite cliques; finally, dispose the isolated edges, get the communities, and complete the division of the bipartite network. This algorithm is different from the traditional community mining one since it uses edges as the community division medium and does not need to specify the number of the division of communities before the experiment. Furthermore, we can find overlapping communities by this new algorithm which can decrease the time complexity.

scholarly journals An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Bader Fahad Alkhamees ◽  
Mogeeb A. A. Mosleh ◽  
Hussain AlSalman ◽  
Muhammad Azeem Akbar
Keyword(s):  
Genetic Algorithm ◽  
Community Detection ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Simulation Experiments ◽  
H Index ◽  
The Hierarchical Structure ◽  
Node Similarity ◽  
Information Recommendation ◽  
Evaluation Metric

The strenuous mining and arduous discovery of the concealed community structure in complex networks has received tremendous attention by the research community and is a trending domain in the multifaceted network as it not only reveals details about the hierarchical structure of multifaceted network but also assists in better understanding of the core functions of the network and subsequently information recommendation. The bipartite networks belong to the multifaceted network whose nodes can be divided into a dissimilar node-set so that no edges assist between the vertices. Even though the discovery of communities in one-mode network is briefly studied, community detection in bipartite networks is not studied. In this paper, we propose a novel Rider-Harris Hawks Optimization (RHHO) algorithm for community detection in a bipartite network through node similarity. The proposed RHHO is developed by the integration of the Rider Optimization (RO) algorithm with the Harris Hawks Optimization (HHO) algorithm. Moreover, a new evaluation metric, i.e., h-Tversky Index (h-TI), is also proposed for computing node similarity and fitness is newly devised considering modularity. The goal of modularity is to quantify the goodness of a specific division of network to evaluate the accuracy of the proposed community detection. The quantitative assessment of the proposed approach, as well as thorough comparative evaluation, was meticulously conducted in terms of fitness and modularity over the citation networks datasets (cit-HepPh and cit-HepTh) and bipartite network datasets (Movie Lens 100 K and American Revolution datasets). The performance was analyzed for 250 iterations of the simulation experiments. Experimental results have shown that the proposed method demonstrated a maximal fitness of 0.74353 and maximal modularity of 0.77433, outperforming the state-of-the-art approaches, including h-index-based link prediction, such as Multiagent Genetic Algorithm (MAGA), Genetic Algorithm (GA), Memetic Algorithm for Community Detection in Bipartite Networks (MATMCD-BN), and HHO.

Community cores expansion for overlapping community detection in complex networks

2018 ◽  
Vol 32 (33) ◽  
pp. 1850405 ◽  
Author(s):  
Yongjie Yan ◽  
Guang Yu ◽  
Xiangbin Yan ◽  
Hui Xie
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Local Structure ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Clique Percolation ◽  
Overlapping Community ◽  
Synthetic Datasets

The identification of communities has attracted considerable attentions in the last few years. We propose a novel heuristic algorithm for overlapping community detection based on community cores in complex networks. We introduce a novel clique percolation algorithm and maximize cliques in the finding overlapping communities (node covers) in graphs. We show how vertices can be used to quantify types of local structure presented in a community and identify group nodes that have similar roles in relation to their neighbors. We compare the approach with other three common algorithms in the analysis of the Zachary’s karate club network and the dolphins network. Experimental results in real-world and synthetic datasets (Lancichinetti–Fortunato–Radicchi (LFR) benchmark networks [A. Lancichinetti and S. Fortunato, Phys. Rev. E 80 (2009) 016118]) demonstrate the model has scalability and is well behaved.

A UNIFIED COMMUNITY DETECTION ALGORITHM IN LARGE-SCALE COMPLEX NETWORKS

2019 ◽  
Vol 22 (03) ◽  
pp. 1950004
Author(s):  
HAO LONG ◽  
XIAO-WEI LIU
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Detection Algorithm ◽  
Component Structure ◽  
Overlapping Communities ◽  
Expansion Strategy ◽  
High Scalability ◽  
Community Detection Algorithm ◽  
Large Scale Networks

A community is the basic component structure of complex networks and is important for network analysis. In recent decades, researchers from different fields have witnessed a boom of community detection, and many algorithms were proposed to retrieve disjoint or overlapping communities. In this paper, a unified expansion approach is proposed to obtain two different network partitions, which can provide divisions with higher accuracies and have high scalability in large-scale networks. First, we define the edge intensity to quantify the densities of network edges, a higher edge intensity indicates a more compact pair of nodes. Second, vertices of higher density edges are extracted out and denoted as core nodes, whereas other vertices are treated as margin nodes; finally we apply an expansion strategy to form disjoint communities: closely connected core nodes are combined as disjoint skeleton communities, and margin nodes are gradually attached to the nearest skeleton communities. To detect overlapping communities, extra steps are adopted: potential overlapping nodes are identified from the existing disjoint communities and replicated; and communities that bear replicas are further partitioned into smaller clusters. Because replicas of potential overlapping nodes might remain in different communities, overlapping communities can be acquired. Experimental results on real and synthetic networks illustrate higher accuracy and better performance of our method.

scholarly journals Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density

2021 ◽  
Vol 13 (4) ◽  
pp. 89
Author(s):  
Yubo Peng ◽  
Bofeng Zhang ◽  
Furong Chang
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Point Of View ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Overlapping Community Detection ◽  
The Real ◽  
Overlapping Community ◽  
Community Detection Algorithm

Community detection plays an essential role in understanding network topology and mining underlying information. A bipartite network is a complex network with more important authenticity and applicability than a one-mode network in the real world. There are many communities in the network that present natural overlapping structures in the real world. However, most of the research focuses on detecting non-overlapping community structures in the bipartite network, and the resolution of the existing evaluation function for the community structure’s merits are limited. So, we propose a novel function for community detection and evaluation of the bipartite network, called community density D. And based on community density, a bipartite network community detection algorithm DSNE (Density Sub-community Node-pair Extraction) is proposed, which is effective for overlapping community detection from a micro point of view. The experiments based on artificially-generated networks and real-world networks show that the DSNE algorithm is superior to some existing excellent algorithms; in comparison, the community density (D) is better than the bipartite network’s modularity.

Using functional dependency and node closure to detect communities from the sparse network

2018 ◽  
Vol 32 (03) ◽  
pp. 1850018
Author(s):  
Yang Zhou ◽  
Haifei Miao ◽  
Wei Liu ◽  
Xiaoyun Chen ◽  
Jianjun Cheng
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Time Complexity ◽  
Functional Dependency ◽  
Experimental Results ◽  
Time Consumption ◽  
Database Theory ◽  
Effectiveness And Efficiency

Community structure is one of the most important features of complex networks, a large number of methods have been proposed to extract community structures from networks. However, some of those methods suffer from the high time complexity, and some of them cannot obtain the acceptable results. In this paper, we borrow the idea from the database theory, and propose the concepts of functional dependency (FD) between nodes and node closure first, then we utilize these concepts to extract communities. This method takes both effectiveness and efficiency into consideration, the community detection process can be accomplished with O(m) time consumption. We conducted extensive experiments both on some synthetic networks and on some real-world networks, the experimental results demonstrate that the method can detect communities from a given network successfully.

Overlapping Community Detection in Bipartite Networks using a Micro-bipartite Network Model: Bi-EgoNet

2019 ◽  
Vol 37 (6) ◽  
pp. 7965-7976 ◽  
Author(s):  
Furong Chang ◽  
Bofeng Zhang ◽  
Yue Zhao ◽  
Songxian Wu ◽  
Guobing Zou ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Model ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Overlapping Community Detection ◽  
Overlapping Community

scholarly journals Community Detection with Self-Adapting Switching Based on Affinity

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Ning-Ning Wang ◽  
Zhen Jin ◽  
Xiao-Long Peng
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Time Complexity ◽  
State Of The Art ◽  
Community Structures ◽  
Computational Simulations ◽  
Network Function ◽  
Detection Algorithms ◽  
New Framework

Community structures in complex networks play an important role in researching network function. Although there are various algorithms based on affinity or similarity, their drawbacks are obvious. They perform well in strong communities, but perform poor in weak communities. Experiments show that sometimes, community detection algorithms based on a single affinity do not work well, especially for weak communities. So we design a self-adapting switching (SAS) algorithm, where weak communities are detected by combination of two affinities. Compared with some state-of-the-art algorithms, the algorithm has a competitive accuracy and its time complexity is near linear. Our algorithm also provides a new framework of combination algorithm for community detection. Some extensive computational simulations on both artificial and real-world networks confirm the potential capability of our algorithm.

scholarly journals Is it correct to project and detect? How weighting unipartite projections influences community detection

2020 ◽  
Vol 8 (S1) ◽  
pp. S145-S163 ◽  
Author(s):  
Tristan J. B. Cann ◽  
Iain S. Weaver ◽  
Hywel T. P. Williams
Keyword(s):  
Social Media ◽  
Community Structure ◽  
Community Detection ◽  
Detection Algorithm ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Algorithm Performance ◽  
Projection Scheme ◽  
Community Detection Algorithm ◽  
Experimental Findings

AbstractBipartite networks represent pairwise relationships between nodes belonging to two distinct classes. While established methods exist for analyzing unipartite networks, those for bipartite network analysis are somewhat obscure and relatively less developed. Community detection in such instances is frequently approached by first projecting the network onto a unipartite network, a method where edges between node classes are encoded as edges within one class. Here we test seven different projection schemes by assessing the performance of community detection on both: (i) a real-world dataset from social media and (ii) an ensemble of artificial networks with prescribed community structure. A number of performance and accuracy issues become apparent from the experimental findings, especially in the case of long-tailed degree distributions. Of the methods tested, the “hyperbolic” projection scheme alleviates most of these difficulties and is thus the most robust scheme of those tested. We conclude that any interpretation of community detection algorithm performance on projected networks must be done with care as certain network configurations require strong community preference for the bipartite structure to be reflected in the unipartite communities. Our results have implications for the analysis of detected community structure in projected unipartite networks.

scholarly journals Similarity-based local community detection for bipartite networks

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1559-1570 ◽  
Author(s):  
Dongming Chen ◽  
Wei Zhao ◽  
Dongqi Wang ◽  
Xinyu Huang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Local Community ◽  
Partial Information ◽  
Real Data ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Local Community Detection

Local community detection aims to obtain the local communities to which target nodes belong, by employing only partial information of the network. As a commonly used network model, bipartite applies naturally when modeling relations between two different classes of objects. There are three problems to be solved in local community detection, such as initial core node selection, expansion approach and community boundary criteria. In this work, a similarity based local community detection algorithm for bipartite networks (SLCDB) is proposed, and the algorithm can be used to detect local community structure by only using either type of nodes of a bipartite network. Experiments on real data prove that SLCDB algorithms output community structure can achieve a very high modularity which outperforms most existing local community detection methods for bipartite networks.

scholarly journals Community detection in bipartite networks using weighted symmetric binary matrix factorization

2015 ◽  
Vol 26 (09) ◽  
pp. 1550096 ◽  
Author(s):  
Zhong-Yuan Zhang ◽  
Yong-Yeol Ahn
Keyword(s):  
Community Detection ◽  
Real World ◽  
Matrix Factorization ◽  
Bipartite Networks ◽  
Binary Matrix ◽  
Overlapping Communities ◽  
Community Membership ◽  
Binary Matrix Factorization ◽  
Generalized Partition ◽  
Node Type

In this paper, we propose weighted symmetric binary matrix factorization (wSBMF) framework to detect overlapping communities in bipartite networks, which describes the relationships between two types of nodes. Our method improves performance by recognizing the distinction between two types of missing edges — ones among the nodes in each node type and the others between two node types. Our method can also explicitly assign community membership and distinguish outliers from overlapping nodes, as well as incorporating existing knowledge on the network. We propose a generalized partition density for bipartite networks as a quality function, which identifies the most appropriate number of communities. The experimental results on both synthetic and real-world networks demonstrate the effectiveness of our method.

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