Networks and Context: Algorithmic Challenges for Context-Aware Social Network Research

Abstract

Social interaction is mediated by computer processes at an ever-increasing rate not least because more and more people have smartphones as their everyday and habitual companions. This enables collection of a vast amount of data containing an unprecedented richness of metadata of interaction and communication. Such context information contains valuable insights for social network research and allows for qualitative grading of network structure and consecutive structural analysis. Due to the complex, heterogeneous, dynamic, and uncertain nature of such information it is yet to be considered for network analysis tasks in its entirety. In this paper, we emphasize how network analysis benefits from considering context information and identify the key challenges that have to be tackled. From an algorithmic perspective, such challenges appear on all steps of a network analysis workflow: Dynamics and uncertainty of information affects modeling networks, calculation of general metrics, calculation of centrality rankings, graph clustering, and visualization. Ultimately, novel algorithms have to be designed to combine context data and structural information to enable future context-aware network research.