Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2015

Hasani-Mavriqi Ilire, Geigl Florian, Pujari Subhash Chandra, Lex Elisabeth, Helic Denis

Influence of Status Social on Consensus Building in Collaboration Networks

In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015), Jian Pei, Fabrizio Silvestri and Jie Tang, ACM/IEEE, Paris, France, 2015

Konferenz
In this paper, we analyze the influence of socialstatus on opinion dynamics and consensus building in collaborationnetworks. To that end, we simulate the diffusion of opinionsin empirical collaboration networks by taking into account boththe network structure and the individual differences of peoplereflected through their social status. For our simulations, weadapt a well-known Naming Game model and extend it withthe Probabilistic Meeting Rule to account for the social statusof individuals participating in a meeting. This mechanism issufficiently flexible and allows us to model various situations incollaboration networks, such as the emergence or disappearanceof social classes. In this work, we concentrate on studyingthree well-known forms of class society: egalitarian, ranked andstratified. In particular, we are interested in the way these societyforms facilitate opinion diffusion. Our experimental findingsreveal that (i) opinion dynamics in collaboration networks isindeed affected by the individuals’ social status and (ii) thiseffect is intricate and non-obvious. In particular, although thesocial status favors consensus building, relying on it too stronglycan slow down the opinion diffusion, indicating that there is aspecific setting for each collaboration network in which socialstatus optimally benefits the consensus building process.
2015

Pujari Subhash Chandra, Hadgu Asmelah Teka, Lex Elisabeth, Jäschke Robert

Social Activity versus Academic Activity: A Case Study of Computer Scientists on Twitter

In Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business (i-KNOW 2015), ACM, Graz, Austria, 2015

Konferenz
In this work, we study social and academic network activities of researchers from Computer Science. Using a recently proposed framework, we map the researchers to their Twitter accounts and link them to their publications. This enables us to create two types of networks: first, networks that reflect social activities on Twitter, namely the researchers’ follow, retweet and mention networks and second, networks that reflect academic activities, that is the co-authorship and citation networks. Based on these datasets, we (i) compare the social activities of researchers with their academic activities, (ii) investigate the consistency and similarity of communities within the social and academic activity networks, and (iii) investigate the information flow between different areas of Computer Science in and between both types of networks. Our findings show that if co-authors interact on Twitter, their relationship is reciprocal, increasing with the numbers of papers they co-authored. In general, the social and the academic activities are not correlated. In terms of community analysis, we found that the three social activity networks are most consistent with each other, with the highest consistency between the retweet and mention network. A study of information flow revealed that in the follow network, researchers from Data Management, HumanComputer Interaction, and Artificial Intelligence act as a source of information for other areas in Computer Science.
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