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Open AccessProceedings Article

Design and analysis of value creation networks

S. Kameshwaran, +2 more
- pp 1551-1554
TLDR
These efforts towards design and analysis of value creation networks are summarized: 1) network representation of interactions and value creations, 2) identify contribution of a node based on values created from various activities, and 3) ranking nodes based on structural properties of interaction and the resulting values.
Abstract
There are many diverse domains like academic collaboration, service industry, and movies, where a group of agents are involved in a set of activities through interactions or collaborations to create value. The end result of the value creation process is two pronged: firstly, there is a cumulative value created due to the interactions and secondly, a network that captures the pattern of historical interactions between the agents. In this paper we summarize our efforts towards design and analysis of value creation networks: 1) network representation of interactions and value creations, 2) identify contribution of a node based on values created from various activities, and 3) ranking nodes based on structural properties of interactions and the resulting values. To highlight the efficacy of our proposed algorithms, we present results on IMDB and services industry data.

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Citations
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Journal ArticleDOI

Outcome aware ranking in value creation networks

TL;DR: A natural ranking problem that arises in settings in which a community of people are engaged in regular interactions with an end goal of creating value is considered and a novel algorithm for computing the ranking is developed.
References
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Journal ArticleDOI

Eigenvector-like measures of centrality for asymmetric relations

TL;DR: An alternative measure of centrality is suggested that equals an eigenvector when eigenvectors can be used and provides meaningfully comparable results when they cannot.
Proceedings ArticleDOI

Outcome aware ranking in interaction networks

TL;DR: A novel ranking technique that was developed in the context of an application that arose in a Service Delivery setting based on extension of eigen value methods and results on real-life, public domain datasets from the Internet Movie DataBase are presented.
Proceedings Article

Analyses for service interaction networks with applications to service delivery

TL;DR: This work focuses on learning individual and team behavior of different people or agents of a service organization by studying the patterns and outcomes of historical interactions and develops the notion of service interaction networks, an abstraction of the historical data that allows one to cast practical problems in a formal setting.
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