Hasani-Mavriqi Ilire, Geigl Florian, Pujari Subhash Chandra, Lex Elisabeth, Helic Denis
2015
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
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.
Seitlinger Paul, Kowald Dominik, Kopeinik Simone, Hasani-Mavriqi Ilire, Ley Tobias, Lex Elisabeth
2015
Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics
In 24rd International World Wide Web Conference, Web-Science Track Aldo Gangemi, Stefano Leonardi and Alessandro Panconesi ACM Florence
Classic resource recommenders like Collaborative Filtering(CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and inter-pretation. In this paper, we propose a novel hybrid rec-ommendation strategy that re nes CF by capturing thesedynamics. The evaluation results reveal that our approachsubstantially improves CF and, depending on the dataset,successfully competes with a computationally much moreexpensive Matrix Factorization variant.