Community Detection in Partially Observable Social Networks

Tran, Cong; Shin, Won-Yong; Spitz, Andreas

doi:10.1145/3461339

Cited by 20 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…В статье [49] В работах [53][54][55] предложена методика KroMFac, с помощью которой проводится обнаружение сообщества методом регуляризованной неотрицательной матричной факторизации (non-negative matrix factorization) на основе графовой модели Кронекера. KroMFac сочетает в себе методы анализа сети и обнаружения сообщества в единой унифицированной структуре.…”

Section: анализ сетевых структур и прогнозирование динамики обществен...unclassified

Models and methods for analyzing complex networks and social network structures

Perova

Grigoriev

Zhukov

2023

Rossijskij tehnologičeskij žurnal

View full text Add to dashboard Cite

Objectives. The study aimed to investigate contemporary models, methods, and tools used for analyzing complex social network structures, both on the basis of ready-made solutions in the form of services and software, as well as proprietary applications developed using the Python programming language. Such studies make it possible not only to predict the dynamics of social processes (changes in social attitudes), but also to identify trends in socioeconomic development by monitoring users’ opinions on important economic and social issues, both at the level of individual territorial entities (for example, districts, settlements of small towns, etc.) and wider regions.Methods. Dynamic models and stochastic dynamics analysis methods, which take into account the possibility of self-organization and the presence of memory, are used along with user deanonymization methods and recommendation systems, as well as statistical methods for analyzing profiles in social networks. Numerical modeling methods for analyzing complex networks and processes occurring in them are considered and described in detail. Special attention is paid to data processing in complex network structures using the Python language and its various available libraries.Results. The specifics of the tasks to be solved in the study of complex network structures and their interdisciplinarity associated with the use of methods of system analysis are described in terms of the theory of complex networks, text analytics, and computational linguistics. In particular, the dynamic models of processes observed in complex social network systems, as well as the structural characteristics of such networks and their relationship with the observed dynamic processes including using the theory of constructing dynamic graphs are studied. The use of neural networks to predict the evolution of dynamic processes and structure of complex social systems is investigated. When creating models describing the observed processes, attention is focused on the use of computational linguistics methods to extract knowledge from text messages of users of social networks.Conclusions. Network analysis can be used to structure models of interaction between social units: people, collectives, organizations, etc. Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. Since communication in social networks almost entirely consists of text messages and various publications, almost all relevant studies use textual analysis methods in conjunction with machine learning and artificial intelligence technologies. Of these, convolutional neural networks demonstrated the best results. However, the use of support vector and decision tree methods should also be mentioned, since these contributed considerably to accuracy. In addition, statistical methods are used to compile data samples and analyze obtained results.

show abstract

Section: анализ сетевых структур и прогнозирование динамики обществен...unclassified

Models and methods for analyzing complex networks and social network structures

Perova

Grigoriev

Zhukov

2023

Rossijskij tehnologičeskij žurnal

View full text Add to dashboard Cite

show abstract

“…Many recent studies have examined the impact of social network users on each other and possible interactions (Boldi et al, 2012; Li, Bu, et al, 2020; Li, Wang, et al, 2020; Tran et al, 2021). Therefore, these networks are modelled as probabilistic graphs, the clustering of which has many applications.…”

Section: Applicationsmentioning

confidence: 99%

A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications

Danesh

Dorrigiv

2023

Expert Systems

View full text Add to dashboard Cite

Given the growth of uncertainty in the real world, analysing probabilistic graphs is crucial. Clustering is one of the most fundamental methods of mining probabilistic graphs to discover the hidden patterns in them. This survey examines an extensive and organized analysis of the clustering techniques of large probabilistic graphs proposed in the literature. First, the definition of probabilistic graphs and modelling them are introduced. Second, the clustering of such graphs and their challenges, such as uncertainty of edges, high dimensions, and the impossibility of applying certain graph clustering techniques directly, are expressed. Then, a taxonomy of clustering approaches is discussed in two main categories: threshold-based and possible worlds-based methods. The techniques presented in each category are explained and examined. Here, these methods are evaluated on real datasets, and their performance is compared with each other. Finally, the survey is summarized by describing some of the applications of probabilistic graph clustering and future research directions.clustering, possible worlds-based methods, probabilistic graph, threshold-based methods | INTRODUCTIONGraphs are powerful tools for modelling large interconnected data networks that are highly developed in the community and the surrounding natural world. Social networks, traffic networks, genome databases, and knowledge graphs are samples of common graphs in which entities are considered nodes, and the connections between them are considered the edges. However, the uncertainty in graph data is an undeniable problem created for various reasons such as noise in measurement methods (Aggarwal & Yu, 2009), vague and inaccurate information sources (Cheng et al., 2015), lack of the required accurate information (Zhu et al., 2015), inference and prediction models (Adar & R'e, 2007), and protecting private information (Boldi et al., 2012). In these cases, the data is represented as an uncertain or probabilistic graph in which uncertainty is assigned to each graph component as a probabilistic value. It means that the probabilistic graph can be due to probabilistic nodes, edges, attributes, or a combination of them (Khan et al., 2018).However, most of the existing probabilistic graphs were formed due to probabilistic edges. Each probabilistic edge indicates the possibility of a connection between two corresponding nodes. For example, in protein interactive networks, the interaction between the two proteins is inferred experimentally and is a probabilistic value. Alternatively, in social networks, complex relationships between users, such as trust or influence, are not directly available and are indirectly inferred from user activity (Li et al., 2021). Therefore, most recent studies in probabilistic graphs have focused on these types of graphs. Accordingly, each edge is labelled with the probability of the presence of that connection.Considering the increasing number of applications with uncertain data, mining probabilistic graphs as uncertain structured data wi...

show abstract

“…The Karate Network is a small network with 34 nodes and 78 edges, where each node represents one club member and each edge represents a connection between two members. The node colors denote the many classes based on their characteristics as well as the characteristics of their nearby nodes, and these various classes denote the various communities inside the club network (Tran et al , 2022). In other words, a class instructor (node 1) and a club administrator (node 34) are the two leaders of the two communities in the Karate Network (Zachary, 1977).…”

Section: Introductionmentioning

confidence: 99%

CommunityGCN: community detection using node classification with graph convolution network

Bhattacharya

Nagwani²,

Tripathi³

2023

DTA

View full text Add to dashboard Cite

PurposeA community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).Design/methodology/approachThis work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.FindingsIn the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.Originality/valueThe experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.

show abstract

Community Detection in Partially Observable Social Networks

Cited by 20 publications

References 48 publications

Models and methods for analyzing complex networks and social network structures

Models and methods for analyzing complex networks and social network structures

A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications

CommunityGCN: community detection using node classification with graph convolution network

Contact Info

Product

Resources

About