Evolutionary clustering and community detection algorithms for social media health surveillance

AbstractSocial media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice,” and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020.

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Community Detection in Multiplex Networks

ACM Computing Surveys ◽

10.1145/3444688 ◽

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.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201342 ◽

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.

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