scholarly journals Community detection in networks: a game-theoretic framework

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Yan Chen ◽  
Xuanyu Cao ◽  
K. J. Ray Liu
Keyword(s):  
Community Detection ◽  
Real World ◽  
Expectation Maximization ◽  
Detection Method ◽  
Detection Algorithm ◽  
Generative Models ◽  
Detection Methods ◽  
Game Theoretic ◽  
Community Detection Algorithm ◽  
General Community

AbstractReal-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks and thus gain more insights into the complicated networks. Strategic nodes belonging to different communities interact with each other to decide mutual links in the networks. Hitherto, various community detection methods have been proposed in the literature, yet none of them takes the strategic interactions among nodes into consideration. Additionally, many real-world observations of networks are noisy and incomplete, i.e., with some missing links or fake links, due to either technology constraints or privacy regulations. In this work, a game-theoretic framework of community detection is established, where nodes interact and produce links with each other in a rational way based on mutual benefits, i.e., maximizing their own utility functions when forming a community. Given the proposed game-theoretic generative models for communities, we present a general community detection algorithm based on expectation maximization (EM). Simulations on synthetic networks and experiments on real-world networks demonstrate that the proposed detection method outperforms the state of the art.

Detecting communities in networks using a Bayesian nonparametric method

2014 ◽  
Vol 28 (28) ◽  
pp. 1450199
Author(s):  
Shengze Hu ◽  
Zhenwen Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Real World ◽  
Detection Method ◽  
Generative Models ◽  
Detection Methods ◽  
Model Parameters ◽  
Nonparametric Method ◽  
Bayesian Nonparametric ◽  
New Community

In the real world, a large amount of systems can be described by networks where nodes represent entities and edges the interconnections between them. Community structure in networks is one of the interesting properties revealed in the study of networks. Many methods have been developed to extract communities from networks using the generative models which give the probability of generating networks based on some assumption about the communities. However, many generative models require setting the number of communities in the network. The methods based on such models are lack of practicality, because the number of communities is unknown before determining the communities. In this paper, the Bayesian nonparametric method is used to develop a new community detection method. First, a generative model is built to give the probability of generating the network and its communities. Next, the model parameters and the number of communities are calculated by fitting the model to the actual network. Finally, the communities in the network can be determined using the model parameters. In the experiments, we apply the proposed method to the synthetic and real-world networks, comparing with some other community detection methods. The experimental results show that the proposed method is efficient to detect communities in networks.

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.

scholarly journals Effective Semisupervised Community Detection Using Negative Information

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Dong Liu ◽  
Dequan Duan ◽  
Shikai Sui ◽  
Guojie Song
Keyword(s):  
Community Detection ◽  
Prior Information ◽  
Detection Method ◽  
Detection Algorithm ◽  
Negative Information ◽  
Discovery Process ◽  
The Past ◽  
Considerable Research ◽  
Specific Community ◽  
Community Detection Algorithm

The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities.

RECODE: SOFTWARE PACKAGE REFACTORING VIA COMMUNITY DETECTION IN BIPARTITE SOFTWARE NETWORKS

2014 ◽  
Vol 17 (07n08) ◽  
pp. 1450006 ◽  
Author(s):  
WEIFENG PAN ◽  
BING LI ◽  
BO JIANG ◽  
KUN LIU
Keyword(s):  
Community Detection ◽  
Software Quality ◽  
Real World ◽  
Software Package ◽  
Object Oriented ◽  
Intrinsic Property ◽  
Detection Algorithm ◽  
Software Complexity ◽  
Novel Approach ◽  
Community Detection Algorithm

It is an intrinsic property of real-world software to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality. So successful software has to be reconditioned from time to time. Though many refactoring approaches have been proposed, only a few of them are performed at the package level. In this paper, we present a novel approach to refactor the package structure of object-oriented (OO) software. It uses weighted bipartite software networks to represent classes, packages, and their dependencies; it proposes a guidance community detection algorithm (GUIDA) to obtain the optimized package structure; and it finally provides a list of classes as refactoring candidates by comparing the optimized package structure with the real package structure. Through a set of experiments we have shown that the proposed approach is able to identify a majority of classes that experts recognize as refactoring candidates, and the benefits of our approach are illustrated in comparison with other two approaches.

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.

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.

scholarly journals A Modularity Degree Based Heuristic Community Detection Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dongming Chen ◽  
Dongqi Wang ◽  
Fangzhao Xia
Keyword(s):  
Computational Complexity ◽  
Community Detection ◽  
Basic Problem ◽  
Optimization Methods ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Resolution Limit ◽  
Measure Function ◽  
Optimization Function ◽  
Community Detection Algorithm

A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm) based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.

Community detection based on preferred mode in bipartite networks

2018 ◽  
Vol 32 (27) ◽  
pp. 1850330
Author(s):  
Guolin Wu ◽  
Changgui Gu ◽  
Lu Qiu ◽  
Huijie Yang
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Bipartite Networks ◽  
Community Structures ◽  
Large Size ◽  
Community Information ◽  
Community Detection Algorithm

Identifying community structures in bipartite networks is a popular topic. People usually focus on one of two modes in bipartite networks when uncovering their community structures. According to this understanding, we design a community detection algorithm based on preferred mode in bipartite networks. This algorithm can select corresponding preferred mode according to specific application scenario and effectively extract community information in bipartite networks. The trials in artificial and real-world networks show that the algorithm based on preferred mode has better performances in both small size of bipartite networks and large size of bipartite networks.

Parameter-free Community Detection through Distance Dynamic Synchronization

2019 ◽  
Vol 62 (11) ◽  
pp. 1625-1638
Author(s):  
Jianbin Huang ◽  
Qingquan Bian ◽  
Heli Sun ◽  
Yaming Yang ◽  
Yu Zhou
Keyword(s):  
Community Detection ◽  
Significant Role ◽  
Real World ◽  
Detection Algorithm ◽  
Parameter Setting ◽  
Cohesion Parameter ◽  
Community Detection Algorithm ◽  
Synthetic Networks

Abstract Community detection plays a significant role in understanding the essence of a network. A recently proposed algorithm Attractor, which is based on distance dynamics, can spot communities effectively, but it depends on a cohesion parameter. Moreover, no efficient way is provided to find an optimal cohesion parameter setting. In this paper, we propose a parameter-free community detection algorithm by synchronizing distances iteratively. In each iteration, the distance of each edge will change dynamically according to the effect generated by its related neighbours. Several iterations later, distances between vertices belonging to the same community will synchronize to 0, while distances between vertices not in the same community will synchronize to 1. Besides, merging and division strategies are built up in the process of community detection. Experiments on both real-world and synthetic networks demonstrate benefits of our method compared to the baseline methods.

Community detection by enhancing community structure in bipartite networks

2019 ◽  
Vol 33 (07) ◽  
pp. 1950076 ◽  
Author(s):  
Wenjie Zhou ◽  
Xingyuan Wang ◽  
Chuan Zhang ◽  
Rui Li ◽  
Chunpeng Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Original Structure ◽  
Bipartite Networks ◽  
Detection Algorithms ◽  
Low Weight ◽  
Community Detection Algorithm

Community detection is one of the primary tools to discover useful information that is hidden in complex networks. Some community detection algorithms for bipartite networks have been proposed from various viewpoints. However, the performance of these algorithms deteriorates when the community structure becomes unclear. Enhancing community structure remains a nontrivial task. In this paper, we propose a community detection algorithm, called ECD, that enhances community structure in bipartite networks. In the proposed ECD, the topology of a network is modified by reducing unnecessary edges that are connected to neighboring low-weight communities. Therefore, an ambiguous community structure is converted into a structure that is much clearer than the original structure. The experimental results on both artificial and real-world networks verify the accuracy and reliability of our algorithm. Compared with existing community detection algorithms using state-of-the-art methods, our algorithm has better performance.

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