Social signature identification of dynamical social networks

Li, Rende; Liu, Jianguo; Guo, Qiang; Zhang, Yicheng

doi:10.1016/j.physa.2018.05.094

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Jensen-Shannon Divergence (short for JSD) [39] is introduced to measure the similarity between real distribution p 1 and MCMC distribution p 2 and is defined as:…”

Section: Jensen-shannon Divergencementioning

confidence: 99%

Reconstruction of Unfolding Sub-Events From Social Media Posts

Guo

Zhang

et al. 2022

Front. Phys.

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Event detection plays a crucial role in social media analysis, which usually concludes sub-event detection and correlation. In this article, we present a method for reconstructing the unfolding sub-event relations in terms of external expert knowledge. First, a Single Pass Clustering method is utilized to summarize massive social media posts. Second, a Label Propagation Algorithm is introduced to detect the sub-event according to the expert labeling. Third, a Word Mover’s Distance method is used to measure the correlation between the relevant sub-events. Finally, the Markov Chain Monte Carlo simulation method is presented to regenerate the popularity of social media posts. The experimental results show that the popularity dynamic of the empirical social media sub-events is consistent with the data generated by the proposed method. The evaluation of the unfolding model is 50.52% ∼ 88% higher than that of the random null model in the case of “Shanghai Tesla self-ignition incident.” This work is helpful for understanding the popularity mechanism of the unfolding events for online social media.

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“…Jensen-Shannon Divergence (short for JSD) [39] is introduced to measure the similarity between real distribution p 1 and MCMC distribution p 2 and is defined as:…”

Section: Jensen-shannon Divergencementioning

confidence: 99%

Reconstruction of Unfolding Sub-Events From Social Media Posts

Guo

Zhang

et al. 2022

Front. Phys.

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View full text Add to dashboard Cite

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Evidence of the persistence and consistency of social signatures

Bond

2022

Appl Netw Sci

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Human social networks are composed of multiple dynamic and overlapping communication networks, in which membership changes over time. However, less well understood are whether and how our communication patterns are similar or different over time and across various modes of communication. Here, we use data on the frequency of phone calls, text messages, and in-person interactions to examine the social signatures of more than 700 students in a university setting. Our analysis shows that although there is substantial turnover in participants’ networks, participants’ social signatures are persistent across time and consistent across communication modes. Further, we find that communication networks that are mediated via phone calls or text messages are more stable than are in-person networks. Our results show that, likely due to limitations in cognitive and emotional resources, people maintain networks of relatively stable size and structure their communication within those networks in predictable patterns. Our findings may help with formalizing social network theories, explaining individual-level attitudes and behaviors and aggregate-level social phenomena, and making predictions and detecting abnormalities in applied fields.

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Universal patterns in egocentric communication networks

Iñiguez,

Heydari,

Kertész

et al. 2023

Nat Commun

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Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at the network and individual levels. Egocentric networks, networks of relationships around an individual, exhibit few strong ties and more weaker ties, as evidenced by electronic communication records. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and driving mechanisms of social tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets of interactions between millions of people during months to years. We find universality in tie strength distributions and their individual-level variation across communication modes, even in channels not reflecting offline social relationships. Via a simple model of egocentric network evolution, we show that the observed universality arises from the competition between cumulative advantage and random choice, two tie reinforcement mechanisms whose balance determines the diversity of tie strengths. Our results provide insight into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.

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Social signature identification of dynamical social networks

Cited by 4 publications

References 45 publications

Reconstruction of Unfolding Sub-Events From Social Media Posts

Reconstruction of Unfolding Sub-Events From Social Media Posts

Evidence of the persistence and consistency of social signatures

Universal patterns in egocentric communication networks

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