### Abstract

We propose an agent centric algorithm that each agent (i.e., node) in a social network can use to estimate each of its neighbor’s degree. The knowledge about the degrees of neighboring nodes is useful for many existing algorithms in social networks studies. For example, algorithms to estimate the diffusion rate of information spread need such information. In many studies, either such degree information is assumed to be available or an overall probabilistic distribution of degrees of nodes is presumed. Furthermore, most of these existing algorithms facilitate a macro-level analysis assuming the entire network is available to the researcher although sampling may be required due to the size of the network. In this paper, we consider the case that the network topology is unknown to individual nodes and therefore each node must estimate the degrees of its neighbors. In estimating the degrees, the algorithm correlates observable activities of neighbors to Bernoulli trials and utilize a power-law distribution to infer unobservable activities. Our algorithm was able to estimate the neighbors’ degrees in 92% accuracy for the 60867 number of nodes. We evaluate the mean squared error of accuracy for the proposed algorithm on a real and a synthetic networks.

Original language | English (US) |
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Title of host publication | PRIMA 2014 |

Subtitle of host publication | Principles and Practice of Multi-Agent Systems - 17th International Conference, Proceedings |

Editors | Yang Xu, Jeremy Pitt, Hoa Khanh Dam, Guido Governatori, Takayuki Ito |

Publisher | Springer Verlag |

Pages | 42-56 |

Number of pages | 15 |

ISBN (Electronic) | 9783319131900 |

State | Published - Jan 1 2014 |

Event | 17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014 - Gold Coast, Australia Duration: Dec 1 2014 → Dec 5 2014 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 8861 |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014 |
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Country | Australia |

City | Gold Coast |

Period | 12/1/14 → 12/5/14 |

### Keywords

- Degree estimations
- Distributed computation
- Online social networks

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

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## Cite this

*PRIMA 2014: Principles and Practice of Multi-Agent Systems - 17th International Conference, Proceedings*(pp. 42-56). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8861). Springer Verlag.