scholarly journals Incremental community discovery via latent network representation and probabilistic inference

2019 ◽  
Vol 62 (6) ◽  
pp. 2281-2300
Author(s):  
Zhe Cui ◽  
Noseong Park ◽  
Tanmoy Chakraborty
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Information Gain ◽  
Ground Truth ◽  
Neighborhood Structure ◽  
Detection Algorithms ◽  
Community Information ◽  
Privacy Constraints ◽  
Two Stages ◽  
Entire Network

AbstractMost of the community detection algorithms assume that the complete network structure $$\mathcal {G}=(\mathcal {V},\mathcal {E})$$G=(V,E) is available in advance for analysis. However, in reality this may not be true due to several reasons, such as privacy constraints and restricted access, which result in a partial snapshot of the entire network. In addition, we may be interested in identifying the community information of only a selected subset of nodes (denoted by $$\mathcal {V}_{{\mathrm{T}}} \subseteq \mathcal {V}$$VT⊆V), rather than obtaining the community structure of all the nodes in $$\mathcal {G}$$G. To this end, we propose an incremental community detection method that repeats two stages—(i) network scan and (ii) community update. In the first stage, our method selects an appropriate node in such a way that the discovery of its local neighborhood structure leads to an accurate community detection in the second stage. We propose a novel criterion, called Information Gain, based on existing network embedding algorithms (Deepwalk and node2vec) to scan a node. The proposed community update stage consists of expectation–maximization and Markov Random Field-based denoising strategy. Experiments with 5 diverse networks with known ground-truth community structure show that our algorithm achieves 10.2% higher accuracy on average over state-of-the-art algorithms for both network scan and community update steps.

scholarly journals Community Detection in Multiplex Networks

2021 ◽  
Vol 54 (3) ◽  
pp. 1-35
Author(s):  
Matteo Magnani ◽  
Obaida Hanteer ◽  
Roberto Interdonato ◽  
Luca Rossi ◽  
Andrea Tagarelli
Keyword(s):  
Community Detection ◽  
Experimental Evaluation ◽  
Network Models ◽  
Ground Truth ◽  
Community Structures ◽  
Multiplex Networks ◽  
Detection Algorithms ◽  
Multiplex Network ◽  
The Right ◽  
Modes Of Interaction

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

Spectral Synchronization Reveals Community Structure

2015 ◽  
Vol 719-720 ◽  
pp. 1198-1202
Author(s):  
Ming Yang Zhou ◽  
Zhong Qian Fu ◽  
Zhao Zhuo
Keyword(s):  
Spectral Analysis ◽  
Community Structure ◽  
Community Detection ◽  
Hierarchical Structure ◽  
Edge Density ◽  
Complex Structures ◽  
Detection Algorithms ◽  
Dimension Space ◽  
Local Synchronization ◽  
Better Than

Practical networks have community and hierarchical structure. These complex structures confuse the community detection algorithms and obscure the boundaries of communities. This paper proposes a delicate method which synthesizes spectral analysis and local synchronization to detect communities. Communities emerge automatically in the multi-dimension space of nontrivial eigenvectors. Its performance is compared to that of previous methods and applied to different practical networks. Our results perform better than that of other methods. Besides, it’s more robust for networks whose communities have different edge density and follow various degree distributions. This makes the algorithm a valuable tool to detect and analysis large practical networks with various community structures.

scholarly journals A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jianjun Cheng ◽  
Wenbo Zhang ◽  
Haijuan Yang ◽  
Xing Su ◽  
Tao Ma ◽  
...  
Keyword(s):  
Decision Making ◽  
Community Structure ◽  
Community Detection ◽  
Centrality Measures ◽  
Ideal Solution ◽  
Centrality Measure ◽  
Detection Algorithms ◽  
Pros And Cons ◽  
Topsis Technique

The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

scholarly journals Community Detection Based on a Preferential Decision Model

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Jinfang Sheng ◽  
Ben Lu ◽  
Bin Wang ◽  
Jie Hu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Decision Model ◽  
Structural Characteristics ◽  
Real Life ◽  
Stable State ◽  
Ground Truth ◽  
Label Propagation ◽  
Comparison Algorithms

The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

scholarly journals Towards effective discovery of natural communities in complex networks and implications in e-commerce

2020 ◽  
Author(s):  
Swarup Chattopadhyay ◽  
Tanmay Basu ◽  
Asit K. Das ◽  
Kuntal Ghosh ◽  
Late C. A. Murthy
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Similarity Measure ◽  
Recommender System ◽  
Data Clustering ◽  
Ground Truth ◽  
Normalized Mutual Information ◽  
Detection Algorithms ◽  
Node Similarity ◽  
Art Community

AbstractAutomated community detection is an important problem in the study of complex networks. The idea of community detection is closely related to the concept of data clustering in pattern recognition. Data clustering refers to the task of grouping similar objects and segregating dissimilar objects. The community detection problem can be thought of as finding groups of densely interconnected nodes with few connections to nodes outside the group. A node similarity measure is proposed here that finds the similarity between two nodes by considering both neighbors and non-neighbors of these two nodes. Subsequently, a method is introduced for identifying communities in complex networks using this node similarity measure and the notion of data clustering. The significant characteristic of the proposed method is that it does not need any prior knowledge about the actual communities of a network. Extensive experiments on several real world and artificial networks with known ground-truth communities are reported. The proposed method is compared with various state of the art community detection algorithms by using several criteria, viz. normalized mutual information, f-measure etc. Moreover, it has been successfully applied in improving the effectiveness of a recommender system which is rapidly becoming a crucial tool in e-commerce applications. The empirical results suggest that the proposed technique has the potential to improve the performance of a recommender system and hence it may be useful for other e-commerce applications.

scholarly journals Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fanrong Meng ◽  
Feng Zhang ◽  
Mu Zhu ◽  
Yan Xing ◽  
Zhixiao Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Dynamic Networks ◽  
Experimental Results ◽  
Extended Version ◽  
High Quality ◽  
Detection Algorithms

Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.

Detecting community structure by belonging intensity analysis of intermediate nodes

2019 ◽  
Vol 30 (11) ◽  
pp. 1950079
Author(s):  
Mengjia Shen ◽  
Dong Lv ◽  
Zhixin Ma
Keyword(s):  
Community Structure ◽  
Communication Networks ◽  
Community Detection ◽  
Real World ◽  
Intensity Analysis ◽  
Human Communication ◽  
Intermediate Node ◽  
Detection Algorithms ◽  
Interactive Behavior

Community structure is a common characteristic of complex networks and community detection is an important methodology to reveal the structure of real-world networks. In recent years, many algorithms have been proposed to detect the high-quality communities in real-world networks. However, these algorithms have shortcomings of performing calculation on the whole network or defining objective function and the number of commonties in advance, which affects the performance and complexity of community detection algorithms. In this paper, a novel algorithm has been proposed to detect communities in networks by belonging intensity analysis of intermediate nodes, named BIAS, which is inspired from the interactive behavior in human communication networks. More specifically, intermediate nodes are middlemen between different groups in social networks. BIAS algorithm defines belonging intensity using local interactions and metrics between nodes, and the belonging intensity of intermediate node in different communities is analyzed to distinguish which community the intermediate node belongs to. The experiments of our algorithm with other state-of-the-art algorithms on synthetic networks and real-world networks have shown that BIAS algorithm has better accuracy and can significantly improve the quality of community detection without prior information.

Enhancing betweenness algorithm for detecting communities in complex networks

2014 ◽  
Vol 28 (09) ◽  
pp. 1450074 ◽  
Author(s):  
Benyan Chen ◽  
Ju Xiang ◽  
Ke Hu ◽  
Yi Tang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Topological Property ◽  
Edge Weight ◽  
Detection Algorithms ◽  
Improved Algorithm

Community structure is an important topological property common to many social, biological and technological networks. First, by using the concept of the structural weight, we introduced an improved version of the betweenness algorithm of Girvan and Newman to detect communities in networks without (intrinsic) edge weight and then extended it to networks with (intrinsic) edge weight. The improved algorithm was tested on both artificial and real-world networks, and the results show that it can more effectively detect communities in networks both with and without (intrinsic) edge weight. Moreover, the technique for improving the betweenness algorithm in the paper may be directly applied to other community detection algorithms.

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.

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