Impact of mutual influence while ranking authors in a co-authorship network

Authors

  • Tehmina Amjad International Islamic University Islamabad, Pakistan
  • Ali Daud International Islamic University Islamabad, Pakistan
  • Atia Akram International Islamic University Islamabad, Pakistan
  • Faqir Muhammed Dept. of Business Administration, Air University, SectorE-9, Islamabad 44000, Pakistan

Keywords:

Academic networks, impact of authors, mutual influence (MI), PageRank, ranking of authors.

Abstract

Online bibliographic databases are providing significant resources to conduct analysis of academic social networks.We believe that work of an author is always influenced by work of his or her co-authors. In this study, we investigatethe impact of productivity and quality of work of an author’s co-authors on his or her ranking along with his owncontribution. We propose mutual influence (MI) based ranking method, which ranks authors based on (1) Publicationsof an author, along with impact of publications of his or her co-authors, (2) Normalized author position based Citationsweight, which is calculated from the citations received by an author with respect to position of his or her name in theco-authors list, (3) MINCC that combines the impact of both factors. A series of experiments has been conducted andresults show that proposed approach has capability to ranks authors in a significant way.

Author Biographies

Tehmina Amjad, International Islamic University Islamabad, Pakistan

Department of Computer Science

Ali Daud, International Islamic University Islamabad, Pakistan

Department of Computer Science

Atia Akram, International Islamic University Islamabad, Pakistan

Department of Computer Science

References

Amjad, T., Ding, Y., Daud, A., Xu, J. & Malic, V.(2015). Topic-based

heterogeneous rank. Scientometrics,104:313-324.

Balog, K., Azzopardi, L. & De Rijke, M. (2006). Formal models for

expert finding in enterprise corpora. ACM, pp. 43–50.

Balog, K. & de Rijke, M. (2008). Non-local evidence for expert

finding. ACM, pp. 489–498.

Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual

Web search engine. Computer Networks and ISDN Systems, 30:107–

Chai, J.C., Hua, P.H., Rousseau, R. & Wan, J.K. (2008). The adapted

pure h-index. In H. Kretschmer & F.Havemann (eds.), Proceedings of

WIS 2008, Berlin.

Daud, A. (2012). Using time topic modeling for semantics-based

dynamic research interest finding. Knowledge Based Systems, 26:154–

Daud, A., Abbasi, R. & Muhammad, F. (2013). Finding rising stars

in social networks, in: Database Systems for Advanced Applications.

Springer, pp. 13–24.

Daud, A., Li, J., Zhou, L. & Muhammad, F. (2010). Temporal expert

finding through generalized time topic modeling. Knowledge Based

Systems, 23:615–625.

Ding, Y. (2011a). Topic-based PageRank on author cocitation networks.

Journal of the Association for Information Science and Technology,

:449–466.

Ding, Y. (2011b). Applying weighted PageRank to author citation

networks. Journal of the Association for Information Science and

Technology, 62:236–245.

Ding, Y., Yan, E., Frazho, A. & Caverlee, J. (2009). PageRank for

ranking authors in co-citation networks. Journal of the Association for

Information Science and Technology, 60:2229–2243.

Egghe, L.(2008). Mathematical theory of the h-and g-index in case

of fractional counting of authorship. Journal of the Association for

Information Science and Technology, 59:1608–1616.

Egghe, L. (2006). An improvement of the H-index: the G-index. ISSI

Newsletter. 2:8–9.

Fiala, D. (2012). Time-aware PageRank for bibliographic networks.

Journal of Informetrics, 6:370–388.

author-document-topic graphs. ACM, pp. 87–96.

Gollapalli, S.D., Mitra, P. & Giles, C.L. (2011). Ranking authors in

digital libraries. ACM, pp. 251–254.

Hirsch, J.E. (2005). An index to quantify an individual’s scientific

research output. Proceedings of National Academy of Science U. S. A.

:16569–16572.

Hu, X., Rousseau, R. & Chen, J. (2010). In those fields where multiple

authorship is the rule, the h-index should be supplemented by rolebased

h-indices. Journal of Information Science, 36:73–85.

Liu, X., Bollen, J., Nelson, M.L. & Van de Sompel, H. (2005).

Co-authorship networks in the digital library research community.

Information Processing and Management, 41:1462–1480.

Liu, X.Z. & Fang, H. (2011). Fairly sharing the credit of multi-authored

papers and its application in the modification of h-index and g-index.

Scientometrics, 91:37–49.

Li, X.L., Foo, C.S., Tew, K.L. & Ng, S.K. (2009). Searching for rising

stars in bibliography networks, in: Database Systems for Advanced

Applications. Springer, pp. 288–292.

Maslov, S. & Redner, S. (2008). Promise and pitfalls of extending

Google’s PageRank algorithm to citation networks. The Journal of

Neuroscience, 28:11103–11105.

Page, L., Brin, S., Motwani, R. & Winograd, T. (1999). The PageRank

citation ranking: bringing order to the web, technical report, Stanford

University, Stanford, CA.

Sekercioglu, C.H. (2008). Quantifying coauthor contributions. Science,

:371.

Sun, Y., Barber, R., Gupta, M., Aggarwal, C.C. & Han, J. (2011). Coauthor

relationship prediction in heterogeneous bibliographic networks,

in: Advances in Social Networks Analysis and Mining (ASONAM),

International Conference on. IEEE, pp. 121–128.

Tang, J., Jin, R. & Zhang, J. (2008a). A topic modeling approach and

its integration into the random walk framework for academic search, in:

Data Mining, 2008. ICDM’08. Eighth IEEE International Conference

on. IEEE, pp. 1055–1060.

Tang, J., Zhang, J., Jin, R., Yang, Z., Cai, K., Zhang, L. & Su, Z.

(2011). Topic level expertise search over heterogeneous networks.

Machine Learning, 82:211–237.

Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L. & Su, Z. (2008b).

Arnetminer: extraction and mining of academic social networks, in:

Proceedings of the 14th ACM SIGKDD International Conference on

Knowledge Discovery and Data Mining. ACM, pp. 990–998.

Umagandhi, R. & Kumar, A.S. (2014). Time heuristics ranking

approach for recommended queries using search engine query logs.

Kuwait Journal of Science, 41:127-149.

Wang, Y., Tong, Y. & Zeng, M. (2013). Ranking scientific articles

by exploiting citations, authors, journals, and time information, in:

Twenty-Seventh AAAI Conference on Artificial Intelligence.

Wan, J.K., Hua, P.H. & Rousseau, R. (2013). The pure h-index:

calculating an author’s h-index by taking co-authors into account.

COLLNET Journal of Scientometrics and Information Management,

:1-5.

Wan, J.K., Hua, P.H. & Rousseau, R. (2007). The pure h-index:

calculating an author’sh-index by taking co-authors into account.

Collnet Journal of Scientometrics and Information Management, 1:

–5.

Wei, W., Barnaghi, P. & Bargiela, A. (2011). Rational Research model

for ranking semantic entities. Information Sciences, 181:2823–2840.

Yan, E. & Ding, Y. (2011). Discovering author impact: A PageRank

perspective. Information Processing and Management, 47:125–134.

Yan, E. & Ding, Y. (2010). Measuring scholarly impact in heterogeneous

networks. American Societyof Information Science and Technology,

:1–7.

Yan, E. & Ding, Y. (2009). Applying centrality measures to impact

analysis: A coauthorship network analysis. Journal of the Association

for Information Science and Technology, 60:2107–2118.

Yan, E., Ding, Y. & Sugimoto, C.R. (2011). P-Rank: An indicator

measuring prestige in heterogeneous scholarly networks. Journal of the

Association for Information Science and Technology, 62:467–477.

Zhou, D., Orshanskiy, S.A., Zha, H. & Giles, C.L. (2007). Coranking

authors and documents in a heterogeneous network, in: Data

Mining, 2007. ICDM 2007. Seventh IEEE International Conference on.

IEEE, pp. 739–744.

Zhou, Y.B., Lü, L. & Li, M. (2012). Quantifying the influence of

scientists and their publications: distinguishing between prestige and

popularity. New Journal of Physics, 14:033033.

Downloads

Published

08-08-2016