Axiomatic foundations for ranking systems
Journal of Artificial Intelligence Research
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Outcome aware ranking in interaction networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Local graph sparsification for scalable clustering
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Sparsification of influence networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A shapley value approach for influence attribution
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
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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.