An ant colony optimization approach to expert identification in social networks

Muhammad Aurangzeb Ahmad, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In a social network there may be people who are experts on a subject. Identifying such people and routing queries to such experts is an important problem. While the degree of separation between any node and an expert node may be small, assuming that social networks are small world networks, not all nodes may be willing to route the query because flooding the network with queries may result in the nodes becoming less likely to route queries in the future. Given this constraint and that there may be time constraints it is imperative to have an efficient way to identify experts in a network and route queries to these experts. In this paper we present an Ant Colony Optimization (ACO) based approach for expert identification and query routing in social networks. Also, even after one has identified the experts in the network, there may be new emerging topics for which there are not identifiable experts in the network. For such cases we extend the basic ACO model and introduce the notion of composibility of pheromones, where trails of different pheromones can be combined to for routing purposes.

Original languageEnglish
Title of host publicationSocial Computing, Behavioral Modeling, and Prediction, 2008
EditorsJohn J. Salerno, Michael J. Young, Huan Liu
PublisherSpringer
Pages120-128
Number of pages9
ISBN (Print)9780387776712
DOIs
Publication statusPublished - 1 Jan 2008
Externally publishedYes
Event1st International workshop on Social Computing, Behavioral Modeling and Prediction, 2008 - Phoenix, United States
Duration: 1 Apr 20082 Apr 2008

Publication series

NameSocial Computing, Behavioral Modeling, and Prediction, 2008

Conference

Conference1st International workshop on Social Computing, Behavioral Modeling and Prediction, 2008
CountryUnited States
CityPhoenix
Period1/4/082/4/08

Fingerprint

Ant colony optimization
Social Networks
Small-world networks
Query
Routing
Pheromone
Vertex of a graph
Small-world Network
Flooding
Optimization Model
Likely

ASJC Scopus subject areas

  • Modelling and Simulation

Cite this

Ahmad, M. A., & Srivastava, J. (2008). An ant colony optimization approach to expert identification in social networks. In J. J. Salerno, M. J. Young, & H. Liu (Eds.), Social Computing, Behavioral Modeling, and Prediction, 2008 (pp. 120-128). (Social Computing, Behavioral Modeling, and Prediction, 2008). Springer. https://doi.org/10.1007/978-0-387-77672-9_14

An ant colony optimization approach to expert identification in social networks. / Ahmad, Muhammad Aurangzeb; Srivastava, Jaideep.

Social Computing, Behavioral Modeling, and Prediction, 2008. ed. / John J. Salerno; Michael J. Young; Huan Liu. Springer, 2008. p. 120-128 (Social Computing, Behavioral Modeling, and Prediction, 2008).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ahmad, MA & Srivastava, J 2008, An ant colony optimization approach to expert identification in social networks. in JJ Salerno, MJ Young & H Liu (eds), Social Computing, Behavioral Modeling, and Prediction, 2008. Social Computing, Behavioral Modeling, and Prediction, 2008, Springer, pp. 120-128, 1st International workshop on Social Computing, Behavioral Modeling and Prediction, 2008, Phoenix, United States, 1/4/08. https://doi.org/10.1007/978-0-387-77672-9_14
Ahmad MA, Srivastava J. An ant colony optimization approach to expert identification in social networks. In Salerno JJ, Young MJ, Liu H, editors, Social Computing, Behavioral Modeling, and Prediction, 2008. Springer. 2008. p. 120-128. (Social Computing, Behavioral Modeling, and Prediction, 2008). https://doi.org/10.1007/978-0-387-77672-9_14
Ahmad, Muhammad Aurangzeb ; Srivastava, Jaideep. / An ant colony optimization approach to expert identification in social networks. Social Computing, Behavioral Modeling, and Prediction, 2008. editor / John J. Salerno ; Michael J. Young ; Huan Liu. Springer, 2008. pp. 120-128 (Social Computing, Behavioral Modeling, and Prediction, 2008).
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