Ranking Mechanisms for Interaction Networks

Interaction networks are prevalent in real world applications and they manifest in several forms such as online social networks, collaboration networks, technological networks, and biological networks. In the analysis of interaction networks, an important aspect is to determine a set of key nodes either with respect to positional power in the network or with respect to behavioral influence. This calls for designing ranking mechanisms to rank nodes/edges in the networks and there exists several well known ranking mechanisms in the literature such as Google page rank and centrality measures in social sciences. We note that these traditional ranking mechanisms are based on the structure of the underlying network. More recently, we witness applications wherein the ranking mechanisms should take into account not only the structure of the network but also other important aspects of the networks such as the value created by the nodes in the network and the marginal contribution of the nodes in the network. Motivated by this observation, the goal of this tutorial is to provide conceptual understanding of recent advances in designing efficient and scalable ranking mechanisms for large interaction networks along with applications to social network analysis.