Complex modeling and analysis of workplace collaboration data

Abstract

Complex networks arise everywhere. Online social networks are famous complex networks examples due to (a) revolutionizing the way people interact on the Web, and (b) permitting in practice the study of interdisciplinary theories that arise from human activities, at both micro (i.e. individual) and macro (i.e. community) level. The vast scale (Big-data) of online human interactions impose certain challenges, such as scalable indexing and efficient retrieval of social data, which are by their nature intertwined in multiple dimensions. In our research we focus on modeling such multidimensional data, mining their intra and inter dependencies to uncover hidden structures and emergent knowledge. In particular, we examine informal interactions at the workplace. Through extensive empirical analysis of corporate communication logs we study users' communication behavioral patterns, dynamics and characteristics, statistical properties and complex correlations between social and topical structures. Our modeling and analysis are not limited to enterprise social data, but are extensible and applicable to other domains, offering a unified framework of complex network modeling and analysis, accurately modeling multiple symmetric or asymmetric, explicit and hidden interaction channels between people.