Building connections: How scientists meet each other during a conference

Génois, Mathieu; Zens, Maria; Lechner, Clemens; Rammstedt, Beatrice; Strohmaier, Markus

doi:10.48550/arxiv.1901.01182

Cited by 6 publications

(22 citation statements)

References 31 publications

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“…• IC2S2-17 26 : Contacts between conference attendees at the International Conference on Computational Social Science 2017 at GESIS in Cologne, Germany on July 11, 2017.…”

Section: Datamentioning

confidence: 99%

“…• WS-16 26 : Contacts between participants at the Computational Social Science Winter Symposium 2016 at GESIS in Cologne, Germany on December 1, 2016.…”

Section: Datamentioning

confidence: 99%

“…For each data set, interaction between individuals occurs in a physical location, and Radio Frequency Identification (RFID) sensors detect a contact when one person is within 1.5 meters of another [24][25][26] . Contacts are recorded at 20-second intervals.…”

Section: Datamentioning

confidence: 99%

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Identifying the temporal dynamics of densification and sparsification in human contact networks

Ferguson¹,

Kobayashi²

2022

Preprint

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Temporal social networks of human interactions are preponderant in understanding the fundamental patterns of human behavior. In these networks, interactions occur locally between individuals (i.e., nodes) who connect with each other at different times, culminating into a complex system-wide web that has a dynamic composition. Dynamic behavior in networks occurs not only locally but also at the global level, as systems expand or shrink due either to: changes in the size of node population or variations in the chance of a connection between two nodes. Here, we propose a numerical maximum-likelihood method to estimate population size and the probability of two nodes connecting at any given point in time. An advantage of the method is that it relies only on aggregate quantities, which are easy to access and free from privacy issues. Our approach enables us to identify the simultaneous (rather than the asynchronous) contribution of each mechanism in the densification and sparsification of human contacts, providing a better understanding of how humans collectively construct and deconstruct social networks.

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“…• IC2S2-17 26 : Contacts between conference attendees at the International Conference on Computational Social Science 2017 at GESIS in Cologne, Germany on July 11, 2017.…”

Section: Datamentioning

confidence: 99%

“…• WS-16 26 : Contacts between participants at the Computational Social Science Winter Symposium 2016 at GESIS in Cologne, Germany on December 1, 2016.…”

Section: Datamentioning

confidence: 99%

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Identifying the temporal dynamics of densification and sparsification in human contact networks

Ferguson¹,

Kobayashi²

2022

Preprint

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“…• WS-16: Contacts between participants of the Computational Social Science Winter Symposium 2016 at GESIS in Cologne on November 30, 2016 19 .…”

Section: Empirical Evidence On Mixed Densification Scalingmentioning

confidence: 99%

“…• IC2S2-17: Contacts between participants of the International Conference on Computational Social Science 2017 at GESIS in Cologne on July 12, 2017 19 .…”

Section: Empirical Evidence On Mixed Densification Scalingmentioning

confidence: 99%

The switching mechanisms of social network densification

Kobayashi,

Génois

2020

Preprint

Self Cite

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Densification and sparsification of temporal networks are attributed to two fundamental mechanisms: a change in the population in the system and/or a change in the chances that nodes in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

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A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

Munir,

Malick,

Ferretti

2024

IEEE Access

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Knowledge Graphs are widely used to represent knowledge structures in complex domains. In most real-world scenarios, these knowledge structures are dynamic. As a result, measures must be developed to assess the robustness and usability of Knowledge Graphs in temporal settings. Additionally, the explainability of inherent knowledge constituents is crucial for the desired attention of Knowledge Graphs, particularly in temporal settings. In this paper, we developed a framework to understand the robustness of factual explainability of Knowledge Graphs. The method is further verified by using meso-level attributes of the knowledge graph. The complex network analysis along with the community structures are co-evaluated through homophilic and heterophilic properties within the graph to validate the robustness of the factual interpretations. The analysis reveals that symbolic representation could be used as a reasonable metric for extracting link-based communities.INDEX TERMS Knowledge graph analysis, complex network analysis, homophily and heterophily analysis, factual reasoning and explainability, interpretability.

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Building connections: How scientists meet each other during a conference

Cited by 6 publications

References 31 publications

Identifying the temporal dynamics of densification and sparsification in human contact networks

Identifying the temporal dynamics of densification and sparsification in human contact networks

The switching mechanisms of social network densification

A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

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