### Abstract

The increase in analysis of real life social networks has led to a better understanding of the ways humans socialize in a group. Since trust is an important part of any social interaction, researchers use such networks to understand the nuances of trust relationships. One of the major requirements in trust applications is identifying the trustworthy actors in these networks. This paper proposes a pair of complementary measures that can be used to measure trust scores of actors in a social network using involvement of social networks. Based on the proposed measures, an iterative matrix convergence algorithm is developed that calculates the trustingness and the trustworthiness of each actor in the network. Trustingness of an actor is defined as the propensity of an actor to trust his neighbors in the network. Trustworthiness, on the other hand, is defined as the willingness of the network to trust an individual actor. The algorithm is proposed based on the idea that a person having higher trustingness score contributes to the trustworthiness of its neighbors to a lower degree. Conversely, a higher trustworthiness score is a result of lots of neighbors linked to the actor having low trustingness scores. The algorithm runs in O(k × E) time where k denotes the number of iterations and E denotes the number of edges in the network. Moreover, the paper shows that the algorithm converges to a finite value quickly. Finally the proposed scores for trust prediction is implemented for various social networks and is shown that the proposed algorithm performs better (average 5%) than the state of the art trust scoring algorithms. The full version of the paper along with the coding implementation can be found at [3].

Original language | English |
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Title of host publication | Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 549-554 |

Number of pages | 6 |

ISBN (Electronic) | 9781509028467 |

DOIs | |

Publication status | Published - 21 Nov 2016 |

Event | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: 18 Aug 2016 → 21 Aug 2016 |

### Other

Other | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
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Country | United States |

City | San Francisco |

Period | 18/8/16 → 21/8/16 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Networks and Communications
- Sociology and Political Science
- Communication

### Cite this

*Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016*(pp. 549-554). [7752289] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752289

**Trustingness & trustworthiness : A pair of complementary trust measures in a social network.** / Roy, Atanu; Sarkar, Chandrima; Srivastava, Jaideep; Huh, Jisu.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016.*, 7752289, Institute of Electrical and Electronics Engineers Inc., pp. 549-554, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 18/8/16. https://doi.org/10.1109/ASONAM.2016.7752289

}

TY - GEN

T1 - Trustingness & trustworthiness

T2 - A pair of complementary trust measures in a social network

AU - Roy, Atanu

AU - Sarkar, Chandrima

AU - Srivastava, Jaideep

AU - Huh, Jisu

PY - 2016/11/21

Y1 - 2016/11/21

N2 - The increase in analysis of real life social networks has led to a better understanding of the ways humans socialize in a group. Since trust is an important part of any social interaction, researchers use such networks to understand the nuances of trust relationships. One of the major requirements in trust applications is identifying the trustworthy actors in these networks. This paper proposes a pair of complementary measures that can be used to measure trust scores of actors in a social network using involvement of social networks. Based on the proposed measures, an iterative matrix convergence algorithm is developed that calculates the trustingness and the trustworthiness of each actor in the network. Trustingness of an actor is defined as the propensity of an actor to trust his neighbors in the network. Trustworthiness, on the other hand, is defined as the willingness of the network to trust an individual actor. The algorithm is proposed based on the idea that a person having higher trustingness score contributes to the trustworthiness of its neighbors to a lower degree. Conversely, a higher trustworthiness score is a result of lots of neighbors linked to the actor having low trustingness scores. The algorithm runs in O(k × E) time where k denotes the number of iterations and E denotes the number of edges in the network. Moreover, the paper shows that the algorithm converges to a finite value quickly. Finally the proposed scores for trust prediction is implemented for various social networks and is shown that the proposed algorithm performs better (average 5%) than the state of the art trust scoring algorithms. The full version of the paper along with the coding implementation can be found at [3].

AB - The increase in analysis of real life social networks has led to a better understanding of the ways humans socialize in a group. Since trust is an important part of any social interaction, researchers use such networks to understand the nuances of trust relationships. One of the major requirements in trust applications is identifying the trustworthy actors in these networks. This paper proposes a pair of complementary measures that can be used to measure trust scores of actors in a social network using involvement of social networks. Based on the proposed measures, an iterative matrix convergence algorithm is developed that calculates the trustingness and the trustworthiness of each actor in the network. Trustingness of an actor is defined as the propensity of an actor to trust his neighbors in the network. Trustworthiness, on the other hand, is defined as the willingness of the network to trust an individual actor. The algorithm is proposed based on the idea that a person having higher trustingness score contributes to the trustworthiness of its neighbors to a lower degree. Conversely, a higher trustworthiness score is a result of lots of neighbors linked to the actor having low trustingness scores. The algorithm runs in O(k × E) time where k denotes the number of iterations and E denotes the number of edges in the network. Moreover, the paper shows that the algorithm converges to a finite value quickly. Finally the proposed scores for trust prediction is implemented for various social networks and is shown that the proposed algorithm performs better (average 5%) than the state of the art trust scoring algorithms. The full version of the paper along with the coding implementation can be found at [3].

UR - http://www.scopus.com/inward/record.url?scp=85006736192&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006736192&partnerID=8YFLogxK

U2 - 10.1109/ASONAM.2016.7752289

DO - 10.1109/ASONAM.2016.7752289

M3 - Conference contribution

AN - SCOPUS:85006736192

SP - 549

EP - 554

BT - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -