Modeling and Predicting Community Structure Changes in Time-Evolving Social Networks

Tajeuna, Etienne Gael; Bouguessa, Mohamed; Wang, Shengrui

doi:10.1109/tkde.2018.2851586

Cited by 46 publications

(21 citation statements)

References 31 publications

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“…The use of social media has exponentially escalated since the late 1990s. The dynamic nature of these platforms has been the reason for their rapid growth, and the structure of these media has facilitated the creation of relationships among users [6,7]. Although individuals often use these networks to meet new people, there is a tendency to connect with those who hold similar expectations or preferences [8].…”

Section: Introductionmentioning

confidence: 99%

“…Recent research on this topic has focused on the relationship between social networks and health issues, both as prevention or educational tools, and as risk factors [6,16]. In this sense, researchers have explored the health-damaging effects of social media [5,15,17], or its side effects, such as isolation, depression, and eating disorders [18,19].…”

Section: Introductionmentioning

confidence: 99%

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A Bibliometric Analysis of the Health Field Regarding Social Networks and Young People

Aparicio-Martínez

Perea-Moreno

Martínez-Jiménez

et al. 2019

IJERPH

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Social networks have historically been used to share information and support regarding health-related topics, and this usage has increased with the rise of online social media. Young people are high users of social media, both as passive listeners and as active contributors. This study aimed to map the trends in publications focused on social networks, health, and young people over the last 40 years. Scopus and the program VOSviewer were used to map the frequency of the publications, keywords, and clusters of researchers active in the field internationally. A structured keyword search using the Scopus database yielded 11,966 publications. The results reveal a long history of research on social networks, health, and young people. Research articles were the most common type of publication (68%), most of which described quantitative studies (82%). The main discipline represented in this literature was medicine, with 6062 documents. North American researchers dominate the field, both as authors and partners in international research collaborations. The present article adds to the literature by elucidating the growing importance of social networks in health research as a topic of study. This may help to inform future investments in public health research and surveillance using these novel data sources.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bibliometric Analysis of the Health Field Regarding Social Networks and Young People

Aparicio-Martínez

Perea-Moreno

Martínez-Jiménez

et al. 2019

IJERPH

View full text Add to dashboard Cite

show abstract

“…For the broad selection, the initial list of 51 papers on DCD methods was used [6, 13-18, 20-23, 27-65]. It was obtained by supplementing 32 temporal trade-off algorithms [6, 13-15, 17, 21, 22, 24, 27-50] from [1] with 19 algorithms not included in the aforementioned survey [16,18,20,23,[51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] that nonetheless possess interesting characteristics with regards to community and evolution extraction. Figure 1 illustrates the relevance of adding those 19 papers as it ensures the inclusion of more recent methods.…”

Section: Algorithm Selectionmentioning

confidence: 99%

A Comparative Study of Community Detection Techniques for Large Evolving Graphs

Coppens

Venter

Mitrović

et al. 2020

Machine Learning and Knowledge Discovery in Databases

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Community detection has recently received increased attention due to its wide range of applications in many fields. While at first most techniques were focused on discovering communities in static networks, lately the focus has shifted toward evolving networks because of their high relevance in real-life problems. Given the increasing number of the methods being proposed, this paper explores the current availability of empirical comparative studies of dynamic methods and also provides its own qualitative and quantitative comparison with the aim of gaining more insight in the performance of available methods. The results show that no single best performing community detection technique exists, but rather, the choice of the method depends on the objective and dataset characteristics.

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“…Here, we give a general overview on evolutionary analysis for dynamic network, which is the most related for this work. The methods of evolutionary analysis for dynamic network are mainly divided into three categories: heuristic approaches [22], [23], machine learning based methods [24], [25] and generative model based methods [10], [15], [27]. Heuristic approaches for evolutionary analysis explore usually the community evolution based on some similar criterions after detecting community structure at snapshots.…”

Section: Related Workmentioning

confidence: 99%

Modeling the Local and Global Evolution Pattern of Community Structures for Dynamic Networks Analysis

Wang

Jiao

et al. 2019

IEEE Access

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Exploring and understanding the temporal structure of dynamic networks attract extensive attention over the past few years. Most of these current research focuses on temporal community detection, evolution analysis or link prediction from a mission-oriented perspective. In fact, these three tasks should be not isolated but mutually reinforcing. Transforming these three tasks into a unified framework, it is crucial to extract the evolution pattern, which helps to understand the time-varying characteristics of temporal structure in essence. In addition, to the best of our knowledge, there is no work focusing on modeling and uncovering the local and global evolution pattern hidden in temporal community structure, simultaneously. In this paper, we propose a novel framework based on Orthogonal Nonnegative Matrix Factorization to Explore the Evolution Pattern (ONMF-EEP) for analyzing and predicting the time-varying structures in dynamic networks from local and global perspectives. The nature of this framework assumes that community structures are subject to a local evolution pattern (LEP) at each snapshot, and these LEPs are from a common global evolution pattern (GEP). The framework can synchronously detect temporal community structure, extract evolution pattern, and predict structure including communities and future snapshot links. The extensive experiments on real-world networks and artificial networks demonstrate that our proposed framework is highly effective on the tasks of dynamic network analysis.INDEX TERMS Orthogonal non-negative matrix factorization (ONMF), temporal community detection, evolutionary pattern extraction, structure prediction.

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Modeling and Predicting Community Structure Changes in Time-Evolving Social Networks

Cited by 46 publications

References 31 publications

A Bibliometric Analysis of the Health Field Regarding Social Networks and Young People

A Bibliometric Analysis of the Health Field Regarding Social Networks and Young People

A Comparative Study of Community Detection Techniques for Large Evolving Graphs

Modeling the Local and Global Evolution Pattern of Community Structures for Dynamic Networks Analysis

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