Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles

Atzmueller, Martin; Doerfel, Stephan; Hotho, Andreas; Mitzlaff, Folke; Stumme, Gerd

doi:10.1007/978-3-642-33684-3_2

Cited by 43 publications

(67 citation statements)

References 29 publications

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“…Confirming previous findings, e.g. in [15], [4], [24], most of the contacts take less than one minute and the contact durations of both conferences show a long-tailed distribution. In addition, Figure 2 shows, that the number of contacts at LWA 2010 was significantly higher than at HT 2011.…”

Section: Rfid Datasupporting

confidence: 90%

“…In addition, Figure 2 shows, that the number of contacts at LWA 2010 was significantly higher than at HT 2011. The diameter and average path length of G is similar to the results presented in [15], [4].…”

Section: Rfid Datasupporting

confidence: 83%

“…After that, we define our problem setting for link detection, and describe, how we model the underlying networks. Next, we present the two datasets collected at the LWA 2010, 3 , and the Hypertext 2011 4 conferences, and provide initial characteristic statistics.…”

Section: B Link Predictionmentioning

confidence: 99%

“…3) We consider (and adapt) different state-of-the-art network proximity measures for the prediction. 4) Finally, we analyze the influence of stronger ties for the prediction and show its impact using real-world data of two conferences. The context of our work is established by the social conferencing application Conferator [3] implemented using the UBICON system.…”

Section: Introductionmentioning

confidence: 99%

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On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties

Scholz

Atzmueller

Stumme

2012

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing

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Abstract-While the analysis of online social networks is a prominent research topic, offline real-world networks are still not covered extensively. However, their analysis can provide important insights into human behavior. In this paper, we analyze influence factors for link prediction in human contact networks. Specifically, we consider the prediction of new links, and extend it to the analysis of recurring links. Furthermore, we consider the impact of stronger ties for the prediction. The results and insights of the analysis are a first step onto predictability applications for human contact networks. I. INTRODUCTIONWith the growing amount of social data, ubiquitous systems, and mobile social media applications transcending everyday life, the analysis of social networks is receiving increased attention. This especially relates to the dynamics and the creation of links between the networks' subjects [20], important aspects of offline social networking still remains largely unexplored. The analysis of such networks can potentially provide more direct answers to fundamental questions, e. g., how do personal links get established, is it possible to correlate this with roles, how does the intensity of personal communication evolve?In this paper, we aim at providing first insights for answering such questions. We focus on real-world offline networks of human contacts, that is, face-to-face conversations between persons. In contrast to virtual networks, these contacts were acquired using a ubiquitous RFID-based system that allows us to collect face-to-face contacts. Thus, we can observe and analyze social interaction at a very detailed level, including the specific event sequences and durations.We [16] in the context of networks of human contacts. We aim to predict new contacts based on network properties, as an adaptation of methods for online social networks. In addition, we extend the analysis in two important directions: First, we consider the length of the contacts in more detail, and analyze the impact of longer conversations. Second, we consider the prediction of future recurring contacts, i. e., renewed contacts between specific actors. For these, we analyze influence factors and patterns for establishing such contacts, and also consider their specific durations in a fine-grained dynamic analysis. Essentially, this leads to the analysis of the impact of stronger ties for new and recurring contacts.

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Section: Rfid Datasupporting

confidence: 90%

Section: Rfid Datasupporting

confidence: 83%

Section: B Link Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties

Scholz

Atzmueller

Stumme

2012

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing

Self Cite

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“…[43], [44], and [51] are the most related studies in the literature to our case study, because these papers carry out posterior visualization and analysis of RFID enriched event data, and furthermore give examples of insight-generating questions whose answers can be obtained through querying the data.…”

Section: Mining Rfid Data From Social Eventsmentioning

confidence: 99%

A Framework for Mining RFID Data From Schedule-Based Systems

Ertek

Zhang

2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-A schedule-based system is a system that operates on or contains within a schedule of events and breaks at particular time intervals. Given RFID data from a schedule-based system, what set of actions and computations, and what type of data mining methods can be applied so that one can obtain actionable insights regarding the system and domain? The research goal of this paper is to answer this posed research question through the development of a framework that systematically produces actionable insights for a given schedule-based system. We show that through integrating appropriate data analysis methodologies as a unified framework, one can obtain many insights from even a very simple RFID dataset, which contains only very few fields. The developed framework is general, and is applicable to any schedule-based system, as long as it operates under a few basic assumptions. The types of insights are also general, and are formulated in the most abstract possible way. The applicability of the developed framework is illustrated through a case study, where real world data from a schedule-based system is analyzed using the introduced framework. Insights obtained include the profiling of entities and events, the interactions between entity and events, and the relations between events.

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