### Abstract

The existing methods for finding influencers use the process of information diffusion to discover the nodes with maximum information spread. These models capture only the process of information diffusion and not the actual social value of collaborations in the network. We have proposed a method for finding influencers using the idea that people generate more value for their work by collaborating with peers of high influence. The social value generated through such collaborations denotes the notion of individual social capital. We hypothesize and show that players with high social capital are often key influencers in the network. We propose a value-allocation model to compute the social capital and allocate the fair share of this capital to each individual involved in the collaboration. We show that our allocation satisfies several axioms of fairness and falls in the same class as the Myerson's allocation function. We implement our allocation rule using an efficient algorithm SoCap and show that our algorithm outperforms the baselines in several real-life data sets. Specifically, in DBLP network, our algorithm outperforms PageRank, PMIA and Weighted Degree baselines up to 8% in terms of precision, recall and F _{1}-measure.

Original language | English |
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Title of host publication | Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 |

Publisher | Association for Computing Machinery |

Pages | 592-599 |

Number of pages | 8 |

ISBN (Print) | 9781450322409 |

DOIs | |

Publication status | Published - 2013 |

Externally published | Yes |

Event | 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 - Niagara Falls, ON Duration: 25 Aug 2013 → 28 Aug 2013 |

### Other

Other | 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 |
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City | Niagara Falls, ON |

Period | 25/8/13 → 28/8/13 |

### ASJC Scopus subject areas

- Computer Networks and Communications
- Information Systems

### Cite this

*Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013*(pp. 592-599). Association for Computing Machinery. https://doi.org/10.1145/2492517.2492552

**Finding influencers in networks using social capital.** / Subbian, Karthik; Sharma, Dhruv; Wen, Zhen; Srivastava, Jaideep.

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

*Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013.*Association for Computing Machinery, pp. 592-599, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Niagara Falls, ON, 25/8/13. https://doi.org/10.1145/2492517.2492552

}

TY - GEN

T1 - Finding influencers in networks using social capital

AU - Subbian, Karthik

AU - Sharma, Dhruv

AU - Wen, Zhen

AU - Srivastava, Jaideep

PY - 2013

Y1 - 2013

N2 - The existing methods for finding influencers use the process of information diffusion to discover the nodes with maximum information spread. These models capture only the process of information diffusion and not the actual social value of collaborations in the network. We have proposed a method for finding influencers using the idea that people generate more value for their work by collaborating with peers of high influence. The social value generated through such collaborations denotes the notion of individual social capital. We hypothesize and show that players with high social capital are often key influencers in the network. We propose a value-allocation model to compute the social capital and allocate the fair share of this capital to each individual involved in the collaboration. We show that our allocation satisfies several axioms of fairness and falls in the same class as the Myerson's allocation function. We implement our allocation rule using an efficient algorithm SoCap and show that our algorithm outperforms the baselines in several real-life data sets. Specifically, in DBLP network, our algorithm outperforms PageRank, PMIA and Weighted Degree baselines up to 8% in terms of precision, recall and F 1-measure.

AB - The existing methods for finding influencers use the process of information diffusion to discover the nodes with maximum information spread. These models capture only the process of information diffusion and not the actual social value of collaborations in the network. We have proposed a method for finding influencers using the idea that people generate more value for their work by collaborating with peers of high influence. The social value generated through such collaborations denotes the notion of individual social capital. We hypothesize and show that players with high social capital are often key influencers in the network. We propose a value-allocation model to compute the social capital and allocate the fair share of this capital to each individual involved in the collaboration. We show that our allocation satisfies several axioms of fairness and falls in the same class as the Myerson's allocation function. We implement our allocation rule using an efficient algorithm SoCap and show that our algorithm outperforms the baselines in several real-life data sets. Specifically, in DBLP network, our algorithm outperforms PageRank, PMIA and Weighted Degree baselines up to 8% in terms of precision, recall and F 1-measure.

UR - http://www.scopus.com/inward/record.url?scp=84893325350&partnerID=8YFLogxK

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U2 - 10.1145/2492517.2492552

DO - 10.1145/2492517.2492552

M3 - Conference contribution

AN - SCOPUS:84893325350

SN - 9781450322409

SP - 592

EP - 599

BT - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013

PB - Association for Computing Machinery

ER -