On the use of intrinsic time scale for dynamic community detection and visualization in social networks

Albano, Alice; Guillaume, Jean‐Loup; Grand, Bénédicte Le

doi:10.1109/rcis.2014.6861033

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…Refs. [1,2,257]). Another measure for a similar purpose-to monitor the growth of the network-is to measure the fraction of nodes or links present at half of the sampling time, or half of the total number of contacts [109].…”

Section: Time Aspects Of Network Evolutionmentioning

confidence: 99%

Modern temporal network theory: a colloquium

2015

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Abstract. The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.

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Section: Time Aspects Of Network Evolutionmentioning

confidence: 99%

Modern temporal network theory: a colloquium

2015

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“…Real-world OSNs, however, are definitely not static. The networks formed in services such as Twitter undergo major and rapid changes over time, which places them in the field of dynamic networks (Asur, Parthasarathy & Ucar, 2007;Palla, Barabasi & Vicsek, 2007;Takaffoli et al, 2011;Tantipathananandh, Berger-Wolf & Kempe, 2007;Roy Chowdhury & Sukumar, 2014;Gauvin, Panisson & Cattuto, 2014;Greene, Doyle & Cunningham, 2010;Aktunc et al, 2015;Albano, Guillaume & Le Grand, 2014). These changes are manifested as users join in or leave one or more communities, by friends mentioning each other to attract attention or by new users referencing a total stranger.…”

Section: Introductionmentioning

confidence: 99%

Exploring Twitter communication dynamics with evolving community analysis

Konstantinidis

Papadopoulos

Kompatsiaris

2017

PeerJ Computer Science

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Online Social Networks (OSNs) have been widely adopted as a means of news dissemination, event reporting, opinion expression and discussion. As a result, news and events are being constantly reported and discussed online through OSNs such as Twitter. However, the variety and scale of all the information renders manual analysis extremely cumbersome, and therefore creating a storyline for an event or news story is an effort-intensive task. The main challenge pertains to the magnitude of data to be analyzed. To this end, we propose a framework for ranking the resulting communities and their metadata on the basis of structural, contextual and evolutionary characteristics such as community centrality, textual entropy, persistence and stability. We apply the proposed framework on three Twitter datasets and demonstrate that the analysis that followed enables the extraction of new insights with respect to influential user accounts, topics of discussion and emerging trends. These insights could primarily assist the work of social and political analysis scientists and the work of journalists in their own story telling, but also highlight the limitations of existing analysis methods and pose new research questions. To our knowledge, this study is the first to investigate the ranking of dynamic communities. In addition, our findings suggest future work regarding the determination of the general context of the communities based on structure and evolutionary behavior alone.

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“…Real-world OSNs, however, are definitely not static. The networks formed in services such as Twitter undergo major and rapid changes over time, which places them in the field of dynamic networks Asur et al (2007); ; Palla et al (2007); Takaffoli et al (2011); Tantipathananandh et al (2007); Roy Chowdhury and Sukumar (2014); Gauvin et al (2014); Greene et al (2010); Aktunc et al (2015); Albano et al (2014). These changes are manifested as users join in or leave one or more communities, by friends mentioning each other to attract attention or by new users referencing a total stranger.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Exploring Twitter communication dynamics with evolving community analysis (v0.1)"

2017

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Online Social Networks (OSNs) have been widely adopted as a means of news dissemination, event reporting, opinion expression and discussion. As a result, news and events are being constantly reported and discussed online through OSNs such as Twitter. However, the variety and scale of all the information renders manual analysis extremely cumbersome, and therefore, creating a storyline for an event or news story is an effortintensive task. The main challenge pertains to the magnitude of data to be analyzed. To this end we propose a framework for ranking the resulting communities and their metadata on the basis of structural, contextual and evolutionary characteristics such as community centrality, textual entropy, persistence and stability. We apply the proposed framework on three Twitter datasets and demonstrate that the analysis that followed enables the extraction of new insights with respect to influential user accounts, topics of discussion and emerging trends. These insights could primarily assist the work of social and political analysis scientists and the work of journalists in their own story telling, but also highlight the limitations of existing analysis methods and pose new research questions. To our knowledge, this study is the first to investigate the ranking of dynamic communities. In addition, our findings suggest future work regarding the determination of the general context of the communities based on structure and evolutionary behavior alone.

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On the use of intrinsic time scale for dynamic community detection and visualization in social networks

Cited by 6 publications

References 27 publications

Modern temporal network theory: a colloquium

Modern temporal network theory: a colloquium

Exploring Twitter communication dynamics with evolving community analysis

Peer Review #1 of "Exploring Twitter communication dynamics with evolving community analysis (v0.1)"

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