scholarly journals A Distributed Hybrid Community Detection Methodology for Social Networks

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 175
Author(s):  
Konstantinos Georgiou ◽  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Community Detection ◽  
Discrete Mathematics ◽  
Real World Data ◽  
Detection Algorithms ◽  
Edge Betweenness ◽  
Social Graphs ◽  
Local Edge ◽  
Available Information ◽  
Content Information ◽  
Vast Range

Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the millions or even billions of vertices. Hence, community detection, where groups of vertices are formed according to a well-defined similarity measure, has never been more essential affecting a vast range of scientific fields such as bio-informatics, sociology, discrete mathematics, nonlinear dynamics, digital marketing, and computer science. Even if an impressive amount of research has yet been published to tackle this NP-hard class problem, the existing methods and algorithms have virtually been proven inefficient and severely unscalable. In this regard, the purpose of this manuscript is to combine the network topology properties expressed by the loose similarity and the local edge betweenness, which is a currently proposed Girvan–Newman’s edge betweenness measure alternative, along with the intrinsic user content information, in order to introduce a novel and highly distributed hybrid community detection methodology. The proposed approach has been thoroughly tested on various real social graphs, roundly compared to other classic divisive community detection algorithms that serve as baselines and practically proven exceptionally scalable, highly efficient, and adequately accurate in terms of revealing the subjacent network hierarchy.

scholarly journals A Distributed Bagging Ensemble Methodology for Community Prediction in Social Networks

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 199 ◽  
Author(s):  
Christos Makris ◽  
Georgios Pispirigos ◽  
Ioannis Orestis Rizos
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Ensemble Methods ◽  
Detection Algorithms ◽  
Edge Betweenness ◽  
Social Graphs ◽  
Published Research ◽  
Vertex Centrality ◽  
Scientific Fields ◽  
Bagging Ensemble

Presently, due to the extended availability of gigantic information networks and the beneficial application of graph analysis in various scientific fields, the necessity for efficient and highly scalable community detection algorithms has never been more essential. Despite the significant amount of published research, the existing methods—such as the Girvan–Newman, random-walk edge betweenness, vertex centrality, InfoMap, spectral clustering, etc.—have virtually been proven incapable of handling real-life social graphs due to the intrinsic computational restrictions that lead to mediocre performance and poor scalability. The purpose of this article is to introduce a novel, distributed community detection methodology which in accordance with the community prediction concept, leverages the reduced complexity and the decreased variance of the bagging ensemble methods, to unveil the subjacent community hierarchy. The proposed approach has been thoroughly tested, meticulously compared against different classic community detection algorithms, and practically proven exceptionally scalable, eminently efficient, and promisingly accurate in unfolding the underlying community structure.

scholarly journals An optimisation tool for robust community detection algorithms using content and topology information

2019 ◽  
Vol 76 (1) ◽  
pp. 226-254 ◽  
Author(s):  
Amhmed Bhih ◽  
Princy Johnson ◽  
Martin Randles
Keyword(s):  
Community Detection ◽  
Real World ◽  
Information Networks ◽  
Detection Methods ◽  
Sources Of Information ◽  
Detection Algorithms ◽  
And Topology ◽  
Node Attribute ◽  
Topology Information ◽  
Content Information

Abstract With the recent prevalence of information networks, the topic of community detection has gained much interest among researchers. In real-world networks, node attribute (content information) is also available in addition to topology information. However, the collected topology information for networks is usually noisy when there are missing edges. Furthermore, the existing community detection methods generally focus on topology information and largely ignore the content information. This makes the task of community detection for incomplete networks very challenging. A new method is proposed that seeks to address this issue and help improve the performance of the existing community detection algorithms by considering both sources of information, i.e. topology and content. Empirical results demonstrate that our proposed method is robust and can detect more meaningful community structures within networks having incomplete information, than the conventional methods that consider only topology information.

Community detection in complex networks using proximate support vector clustering

2018 ◽  
Vol 32 (07) ◽  
pp. 1850101 ◽  
Author(s):  
Feifan Wang ◽  
Baihai Zhang ◽  
Senchun Chai ◽  
Yuanqing Xia
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Support Vector ◽  
Data Sets ◽  
Real World Data ◽  
Support Vector Clustering ◽  
Detection Algorithms ◽  
Vector Clustering ◽  
Proximity Graph ◽  
Vector Approach

Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

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.

Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

2021 ◽  
pp. 1-17
Author(s):  
Mohammed Al-Andoli ◽  
Wooi Ping Cheah ◽  
Shing Chiang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Learning Community ◽  
Community Detection ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Real World Datasets ◽  
The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

scholarly journals A UNIFIED COMMUNITY DETECTION, VISUALIZATION AND ANALYSIS METHOD

2014 ◽  
Vol 17 (01) ◽  
pp. 1450001 ◽  
Author(s):  
MICHEL CRAMPES ◽  
MICHEL PLANTIÉ
Keyword(s):  
Community Detection ◽  
Directed Graphs ◽  
Real Data ◽  
Semantic Interpretation ◽  
Detection Methods ◽  
Analysis Method ◽  
Overlapping Communities ◽  
Social Graphs ◽  
Tractography Data ◽  
Research Domain

With the widespread social networks on the Internet, community detection in social graphs has recently become an important research domain. Interest was initially limited to unipartite graph inputs and partitioned community outputs. More recently, bipartite graphs, directed graphs and overlapping communities have all been investigated. Few contributions however have encompassed all three types of graphs simultaneously. In this paper, we present a method that unifies community detection for these three types of graphs while at the same time it merges partitioned and overlapping communities. Moreover, the results are visualized in a way that allows for analysis and semantic interpretation. For validation purposes this method is experimented on some well-known simple benchmarks and then applied to real data: photos and tags in Facebook and Human Brain Tractography data. This last application leads to the possibility of applying community detection methods to other fields such as data analysis with original enhanced performances.

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