Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases Cover

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Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases

Journal of Data and Information Science

Volume 1 (2016): Issue 3 (August 2016)

By: Raf Guns

Open Access

|Sep 2017

Figures & Tables

Example: Bipartite author-publication network and its corresponding weighted and unweighted co-authorship networks.

Comparison of two definitions of weighted common neighbors.

Example network with three paths between u and v.

Hypothetical example: two cases of author–publication networks.

Influence of tuning parameter α on graph distance predictor (UA).

Comparison of Katz predictor on unweighted, weighted, and bipartite network (UA, Fmax).

Comparison of Katz predictor on unweighted, weighted, and bipartite network (INF, Fmax).

Comparison of Katz predictor on unweighted, weighted, and bipartite network (INF, P@20).

Comparison of rooted PageRank predictor on unweighted, weighted, and bipartite network (UA, Fmax).

Comparison of rooted PageRank predictor on unweighted, weighted, and bipartite network (UA, P@20).

Comparison of rooted PageRank predictor on unweighted, weighted, and bipartite network (INF, Fmax).

Comparison of rooted PageRank predictor on unweighted, weighted, and bipartite network (INF, P@20).

Descriptive statistics of the two datasets_

		UA	INF
Training	Number of authors	1,102	397
	Number of papers	7,569	1,118
	Average number of papers per author	11.9	4.3
	Average number of authors per paper	1.7	1.5
Test	Number of authors	1,102	397
	Number of papers	7,939	1,069
	Average number of papers per author	12.8	4.4
	Average number of authors per paper	1.8	1.6

Results for positive stability_

	UA		INF

	F_max	P@20	F_max	P@20
Reoccurrence	0.59	0.48	0.61	0.48
Reoccurrence with weights	0.59	1.00	0.61	0.81
Reoccurrence with author numbers	0.55	0.52	0.61	0.57

Results for neighborhood-based predictors (UA)_

		Unweighted	Weighted	Bipartite
Common neighbors	F_max	0.3964	0.3928	0.5866
	P@20	1	1	1
Cosine	F_max	0.4025	0.397	0.5865
	P@20	0.1429	0.0952	0.7619

Results for neighborhood-based predictors (INF)_

		Unweighted	Weighted	Bipartite
Common neighbors	F_max	0.4091	0.3935	0.6109
	P@20	0.8571	0.5714	0.8095
Cosine	F_max	0.4332	0.4305	0.6217
	P@20	0.0476	0.0476	1

DOI: https://doi.org/10.20309/jdis.201620 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X

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Language: English

Page range: 59 - 78

Submitted on: May 17, 2016

Accepted on: Jun 26, 2016

Published on: Sep 1, 2017

Published by: Chinese Academy of Sciences, National Science Library

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Network evolution,

Link prediction,

Weighted networks,

Bipartite networks,

Two-mode networks

Related subjects:

Computer sciences,

Information technology,

Project management,

Databases and data mining

© 2017 Raf Guns, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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