Characterizing Knowledge-Transfer Relationships in Dynamic Attributed Networks

Abstract

Characterizing dynamic interactions is currently an important issue when analyzing complex social networks. In this paper, we reinforce the importance of social concepts as the strategic positioning of an actor in a social structure, thus bringing new insights to the analysis of complex networks. Specifically, we propose a new method to characterize relationships based on temporal node-attributes that captures how knowledge is transferred across the network. As a result, we unveil the differences of social relationships in different academic social networks and Q&A communities. We also validate our social definitions in terms of the importance of the edges as assessed by the betweenness centrality metric and compare our results with those of two existing methods. Finally, we apply our method to a ranking task in order to measure the academic importance of researchers.