Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 11th international conference on World Wide Web
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Node ranking in labeled directed graphs
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Concept-Aware Ranking: Teaching an Old Graph New Moves
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Co-authorship networks in the digital library research community
Information Processing and Management: an International Journal - Special issue: Infometrics
Social ranking for spoken web search
Proceedings of the 20th ACM international conference on Information and knowledge management
Ranking mechanisms for interaction networks
Proceedings of the 17th International Conference on Management of Data
| Hi-index | 0.00 |
In this paper, we present a novel ranking technique that we developed in the context of an application that arose in a Service Delivery setting. We consider the problem of ranking agents of a service organization. The service agents typically need to interact with other service agents to accomplish the end goal of resolving customer requests. Their ranking needs to take into account two aspects: firstly, their importance in the network structure that arises as a result of their interactions, and secondly, the value generated by the interactions involving them. We highlight several other applications which have the common theme of ranking the participants of a value creation process based on the network structure of their interactions and the value generated by their interactions. We formally present the problem and describe the modeling technique which enables us to encode the value of interaction in the graph. Our ranking algorithm is based on extension of eigen value methods. We present experimental results on real-life, public domain datasets from the Internet Movie DataBase. This makes our experiments replicable and verifiable.