Social Network Analysis and Mining

, Volume 3, Issue 4, pp 981–999 | Cite as

Robust features of trust in social networks

  • Zoheb Hassan Borbora
  • Muhammad Aurangzeb Ahmad
  • Jehwan Oh
  • Karen Zita Haigh
  • Jaideep Srivastava
  • Zhen Wen
Original Article


We identify robust features of trust in social networks; these are features which are discriminating yet uncorrelated and can potentially be used to predict trust formation between agents in other social networks. The features we investigate are based on an agent’s individual properties as well as those based on the agent’s location within the network. In addition, we analyze features which take into account the agent’s participation in other social interactions within the same network. Three datasets were used in our study—Sony Online Entertainment’s EverQuest II game dataset, a large email network with sentiments and the publicly available Epinions dataset. The first dataset captures activities from a complex persistent game environment characterized by several types of in-game social interactions, whereas the second dataset has anonymized information about people’s email and instant messaging communication. We formulate the problem as one of the link predictions, intranetwork and internetwork, in social networks. We first build machine learning models and then perform an ablation study to identify robust features of trust. Results indicate that shared skills and interests between two agents, their level of activity and level of expertise are the top three predictors of trust in a social network. Furthermore, if only network topology information were available, then an agent’s propensity to connect or communicate, the cosine similarity between two agents and shortest distance between them are found to be the top three predictors of trust. In our study, we have identified the generic characteristics of the networks used as well as the features investigated so that they can be used as guidelines for studying the problem of predicting trust formation in other social networks.


Trust formation Link prediction Feature selection Ablation study 



The research reported herein was supported by the AFRL via Contract No. FA8650-10-C-7010, the ARL Network Science CTA via BBN TECH/W911NF-09-2-0053 and by DARPA via Grant Number W911NF-12-C-0028. The data used for this research were provided by the Sony Online Entertainment. We gratefully acknowledge all our sponsors. We would also like to thank Nishith Pathak for his valuable critique and feedback while writing the paper. The findings presented do not in any way represent, either directly or through implication, the policies of these organizations.


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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Zoheb Hassan Borbora
    • 1
  • Muhammad Aurangzeb Ahmad
    • 1
  • Jehwan Oh
    • 1
  • Karen Zita Haigh
    • 2
  • Jaideep Srivastava
    • 1
  • Zhen Wen
    • 3
  1. 1.Department of Computer ScienceUniversity of MinnesotaMinneapolisUSA
  2. 2.Raytheon BBN TechnologiesLouis ParkUSA
  3. 3.IBM Thomas J. Watson Research CenterHawthorneUSA

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