Capturing missing edges in social networks using vertex similarity

Hung Hsuan Chen; Liang Gou; Xiaolong Zhang; Clyde Lee Giles

doi:10.1145/1999676.1999722

Capturing missing edges in social networks using vertex similarity

Hung Hsuan Chen, Liang Gou, Xiaolong Zhang, Clyde Lee Giles

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

11 Scopus citations

Abstract

We introduce the graph vertex similarity measure, Relation Strength Similarity (RSS), that utilizes a network's topology to discover and capture similar vertices. The RSS has the advantage that it is asymmetric; can be used in a weighted network; and has an adjustable "discovery range" parameter that enables exploration of friend of friend connections in a social network. To evaluate RSS we perform experiments on a coauthorship network from the CiteSeerX database. Our method significantly outperforms other vertex similarity measures in terms of the ability to predict future coauthoring behavior among authors in the CiteSeerX database for the near future 0 to 4 years out and reasonably so for 4 to 6 years out.

Original language	English (US)
Title of host publication	KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference
Pages	195-196
Number of pages	2
DOIs	https://doi.org/10.1145/1999676.1999722
State	Published - 2011
Event	6th International Conference on Knowledge Capture, KCAP 2011 - Banff, AB, Canada Duration: Jun 26 2011 → Jun 29 2011

Publication series

Name	KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference

Other

Other	6th International Conference on Knowledge Capture, KCAP 2011
Country/Territory	Canada
City	Banff, AB
Period	6/26/11 → 6/29/11

All Science Journal Classification (ASJC) codes

Computer Science Applications
Information Systems

Access to Document

10.1145/1999676.1999722

Cite this

@inproceedings{d9e0f9aeceb34bb2a3d13e3e50d98c07,

title = "Capturing missing edges in social networks using vertex similarity",

abstract = "We introduce the graph vertex similarity measure, Relation Strength Similarity (RSS), that utilizes a network's topology to discover and capture similar vertices. The RSS has the advantage that it is asymmetric; can be used in a weighted network; and has an adjustable {"}discovery range{"} parameter that enables exploration of friend of friend connections in a social network. To evaluate RSS we perform experiments on a coauthorship network from the CiteSeerX database. Our method significantly outperforms other vertex similarity measures in terms of the ability to predict future coauthoring behavior among authors in the CiteSeerX database for the near future 0 to 4 years out and reasonably so for 4 to 6 years out.",

author = "Chen, {Hung Hsuan} and Liang Gou and Xiaolong Zhang and Giles, {Clyde Lee}",

year = "2011",

doi = "10.1145/1999676.1999722",

language = "English (US)",

isbn = "9781450303965",

series = "KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference",

pages = "195--196",

booktitle = "KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference",

note = "6th International Conference on Knowledge Capture, KCAP 2011 ; Conference date: 26-06-2011 Through 29-06-2011",

}

Chen, HH, Gou, L, Zhang, X & Giles, CL 2011, Capturing missing edges in social networks using vertex similarity. in KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference. KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference, pp. 195-196, 6th International Conference on Knowledge Capture, KCAP 2011, Banff, AB, Canada, 6/26/11. https://doi.org/10.1145/1999676.1999722

TY - GEN

T1 - Capturing missing edges in social networks using vertex similarity

AU - Chen, Hung Hsuan

AU - Gou, Liang

AU - Zhang, Xiaolong

AU - Giles, Clyde Lee

PY - 2011

Y1 - 2011

N2 - We introduce the graph vertex similarity measure, Relation Strength Similarity (RSS), that utilizes a network's topology to discover and capture similar vertices. The RSS has the advantage that it is asymmetric; can be used in a weighted network; and has an adjustable "discovery range" parameter that enables exploration of friend of friend connections in a social network. To evaluate RSS we perform experiments on a coauthorship network from the CiteSeerX database. Our method significantly outperforms other vertex similarity measures in terms of the ability to predict future coauthoring behavior among authors in the CiteSeerX database for the near future 0 to 4 years out and reasonably so for 4 to 6 years out.

AB - We introduce the graph vertex similarity measure, Relation Strength Similarity (RSS), that utilizes a network's topology to discover and capture similar vertices. The RSS has the advantage that it is asymmetric; can be used in a weighted network; and has an adjustable "discovery range" parameter that enables exploration of friend of friend connections in a social network. To evaluate RSS we perform experiments on a coauthorship network from the CiteSeerX database. Our method significantly outperforms other vertex similarity measures in terms of the ability to predict future coauthoring behavior among authors in the CiteSeerX database for the near future 0 to 4 years out and reasonably so for 4 to 6 years out.

UR - http://www.scopus.com/inward/record.url?scp=79960217006&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79960217006&partnerID=8YFLogxK

U2 - 10.1145/1999676.1999722

DO - 10.1145/1999676.1999722

M3 - Conference contribution

AN - SCOPUS:79960217006

SN - 9781450303965

T3 - KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference

SP - 195

EP - 196

BT - KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference

T2 - 6th International Conference on Knowledge Capture, KCAP 2011

Y2 - 26 June 2011 through 29 June 2011

ER -

Capturing missing edges in social networks using vertex similarity

Abstract

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All Science Journal Classification (ASJC) codes

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