### Abstract

Discovering similar objects in a social network has many interesting issues. Here, we present ASCOS, an Asymmetric Structure COntext Similarity measure that captures the similarity scores among any pairs of nodes in a network. The definition of ASCOS is similar to that of the well-known SimRank since both define score values recursively. However, we show that ASCOS outputs a more complete similarity score than SimRank because SimRank (and several of its variations, such as PRank and SimFusion) on average ignores half paths between nodes during calculation. To make ASCOS tractable in both computation time and memory usage, we propose two variations of ASCOS: a low rank approximation based approach and an iterative solver Gauss-Seidel for linear equations. When the target network is sparse, the run time and the required computing space of these variations are smaller than computing SimRank and ASCOS directly. In addition, the iterative solver divides the original network into several independent sub-systems so that a multi-core server or a distributed computing environment, such as MapReduce, can efficiently solve the problem. We compare the performance of ASCOS with other global structure based similarity measures, including SimRank, Katz, and LHN. The experimental results based on user evaluation suggest that ASCOS gives better results than other measures. In addition, the asymmetric property has the potential to identify the hierarchical structure of a network. Finally, variations of ASCOS (including one distributed variation) can also reduce computation both in space and time.

Original language | English (US) |
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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 | 442-449 |

Number of pages | 8 |

ISBN (Print) | 9781450322409 |

DOIs | |

State | Published - Jan 1 2013 |

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

### Publication series

Name | Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 |
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### Other

Other | 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 |
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Country | Canada |

City | Niagara Falls, ON |

Period | 8/25/13 → 8/28/13 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Information Systems
- Computer Networks and Communications

### Cite this

*Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013*(pp. 442-449). (Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013). Association for Computing Machinery. https://doi.org/10.1145/2492517.2492539

}

*Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013.*Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Association for Computing Machinery, pp. 442-449, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Niagara Falls, ON, Canada, 8/25/13. https://doi.org/10.1145/2492517.2492539

**ASCOS : An Asymmetric network Structure COntext Similarity measure.** / Chen, Hung Hsuan; Giles, C. Lee.

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

TY - GEN

T1 - ASCOS

T2 - An Asymmetric network Structure COntext Similarity measure

AU - Chen, Hung Hsuan

AU - Giles, C. Lee

PY - 2013/1/1

Y1 - 2013/1/1

N2 - Discovering similar objects in a social network has many interesting issues. Here, we present ASCOS, an Asymmetric Structure COntext Similarity measure that captures the similarity scores among any pairs of nodes in a network. The definition of ASCOS is similar to that of the well-known SimRank since both define score values recursively. However, we show that ASCOS outputs a more complete similarity score than SimRank because SimRank (and several of its variations, such as PRank and SimFusion) on average ignores half paths between nodes during calculation. To make ASCOS tractable in both computation time and memory usage, we propose two variations of ASCOS: a low rank approximation based approach and an iterative solver Gauss-Seidel for linear equations. When the target network is sparse, the run time and the required computing space of these variations are smaller than computing SimRank and ASCOS directly. In addition, the iterative solver divides the original network into several independent sub-systems so that a multi-core server or a distributed computing environment, such as MapReduce, can efficiently solve the problem. We compare the performance of ASCOS with other global structure based similarity measures, including SimRank, Katz, and LHN. The experimental results based on user evaluation suggest that ASCOS gives better results than other measures. In addition, the asymmetric property has the potential to identify the hierarchical structure of a network. Finally, variations of ASCOS (including one distributed variation) can also reduce computation both in space and time.

AB - Discovering similar objects in a social network has many interesting issues. Here, we present ASCOS, an Asymmetric Structure COntext Similarity measure that captures the similarity scores among any pairs of nodes in a network. The definition of ASCOS is similar to that of the well-known SimRank since both define score values recursively. However, we show that ASCOS outputs a more complete similarity score than SimRank because SimRank (and several of its variations, such as PRank and SimFusion) on average ignores half paths between nodes during calculation. To make ASCOS tractable in both computation time and memory usage, we propose two variations of ASCOS: a low rank approximation based approach and an iterative solver Gauss-Seidel for linear equations. When the target network is sparse, the run time and the required computing space of these variations are smaller than computing SimRank and ASCOS directly. In addition, the iterative solver divides the original network into several independent sub-systems so that a multi-core server or a distributed computing environment, such as MapReduce, can efficiently solve the problem. We compare the performance of ASCOS with other global structure based similarity measures, including SimRank, Katz, and LHN. The experimental results based on user evaluation suggest that ASCOS gives better results than other measures. In addition, the asymmetric property has the potential to identify the hierarchical structure of a network. Finally, variations of ASCOS (including one distributed variation) can also reduce computation both in space and time.

UR - http://www.scopus.com/inward/record.url?scp=84893307393&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893307393&partnerID=8YFLogxK

U2 - 10.1145/2492517.2492539

DO - 10.1145/2492517.2492539

M3 - Conference contribution

AN - SCOPUS:84893307393

SN - 9781450322409

T3 - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013

SP - 442

EP - 449

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 -