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By Soumajit Pramanik Guide : Dr. Bivas Mitra. Important Author-based Metrics: In-Citation Count H-Index etc.

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Presentation on theme: "By Soumajit Pramanik Guide : Dr. Bivas Mitra. Important Author-based Metrics: In-Citation Count H-Index etc."— Presentation transcript:

1 By Soumajit Pramanik Guide : Dr. Bivas Mitra

2 Important Author-based Metrics: In-Citation Count H-Index etc.


4  Previous works on Citation Network mainly focused on: ◦ Analyzing the evolution of citation and collaboration networks using “Preferential Attachment” [Barabasi et al. 2002] ◦ Understanding the importance of community structure in citation networks [Chin et al. 2006] ◦ Studying the evolution of research topics [He et al. 2009]

5  Previous works on Collaboration Network mainly focused on: ◦ Adopting social network measures of degree, closeness, betweenness and eigenvector centrality to explore individuals’ positions in a given co- authorship network [Liu et al. 2005]. ◦ Analyzing the importance of the geographical proximity (same university/city/country etc.) of the collaborators [Divakarmurthy et al. 2011].

6 1. Existing studies focused on the dominant factors like preferential attachment 2. None of these factors can be self- regulated. 3. Does their exist any self-tunable factor (suppressed by dominant factors) for boosting own citations/collaboration?

7 Advantage of attending Conferences: Face-to-Face interactions with Fellow Scientists Studying the influence of such interactions on the evolution of Citation and Collaboration Networks

8  The authors, whose talks are scheduled in the same technical session of a conference, have high chances of interaction.  In general, the first or the last author (or sometimes both) of a paper attends the conference.

9  Citations & Collaborations: ◦ DBLP Dataset for Computer Science domain (1960- 2008) ◦ Around 1 million papers along with information about author, year, venue and references ◦ 501060 authors tagged with continents (using Microsoft Academic Search) ◦ 6559415 author-wise citation links

10  Interactions: ◦ Two domains: 1> Networking & Distributed Computing 2> Artificial Intelligence ◦ Selected 3 leading conferences from each domain: 1> INFOCOM, ICDCS, IPDPS from the first domain (1982- 2007) 2> AAAI, ICRA, ICDE from the second domain (1980-2008) ◦ Collected session information from DBLP and program schedule of the conferences

11  To regulate some important parameters and manifest their effects on the citation network  Followed statistics regarding articles per field per year, distribution of the number of authors in a paper and citation information from the real dataset  Only tunable parameter used: Successful interaction Rate p (p=0.1,0.2,…,1)

12  Multiplex Network Construction: For each year t: ◦ Citation Layer: Directed author-wise citation links created at t, pointing to papers published before t (or sometimes, in t) ◦ Interaction Layer: Undirected interaction links between authors presenting in same sessions in selected conferences in t ◦ Co-authorship Layer: Undirected collaboration links between two authors if they co-author a paper published in those chosen conferences in t


14  1. Conversion Rate (C R ) for a conference C for a time-span T: No. of “Successful” interactions in C during T -------------------------------------- Total no. of interactions in C during T From this, the definition of the Overall Conversion rate can be simply extended.

15  2. Induced Citation Link Repetition (L R ): L R measures the no. of times each “induced” citation link appears within the recorded time period.  3. Lifespan of Induced citation (L S ): The Lifespan of an “induced” citation is measured as the difference between the first and the last appearing year of the “induced” citation link.

16  4. Rate of appearance (R A ): The rate of appearance of the of a induced citation link is denoted by the ratio of the repetition count and lifespan. Hence R A = L R / L S  5. Influence of successful interaction (I G ): The influence of a “successful” interaction is measured as the latency between the “successful” interaction and the formation of the first induced citation.

17 Interactions to Citations

18  Real Datasets: Networking Domain: 2.87% (381 out of 13240) for [0.9,0.1] interaction probabilities AI Domain: 2.1% (1291 out of 61896) for [0.9,0.1] interaction probabilities

19  Synthetic Dataset: Downfall near end years due to “Boundary Effect”

20 Networking Domain: 1. Overall Value increasing 2. Distributed Contribution AI Domain: 1. Overall Value slowly increasing 2. Dominated Contribution

21 In both domains, 1.Power-Law distribution 2.A significant no. of “induced” citations repeat a high no. of times AI Domain Networking Domain Significant no. of “induced” citations have high R A values Reasons can be a) Low L S or/and b) High L R AI Domain Networking Domain

22 AI Domain Networking Domain 1. High R A ratio results from mainly low L S 2. Ä large no. of induced" citations missing from the right side of the plot due to the boundary effect. 1. Aperiodicity of repetitions of “induced” citations increase almost linearly with their Lifespan 2. High L R not necessarily imply high standard deviation AI Domain Networking Domain

23  Influence Gap (I G )  Influence of Continents 1.All the highly repeating “induced” citations have low “Influence” Gap Dominance of North America-North America pairs AI Domain Networking Domain Networking Domain

24 L R vs L S Standard Deviation vs L S L R vs I G L S vs I G Artificial Intelligence 0.570.98-0.13-0.12 Networking & Distributed Systems 0.610.97-0.14-0.13

25 Citations To Collaborations

26  Conversion Rates ◦ 1. Considered only collaboration between established researchers (having at least 1 publication) ◦ 2. In Networking domain out of 8920 co-author links, 2495 (28%) exhibits a past history of mutual citations! ◦ 3. In AI domain 3211 out of 10192 (31.5%) are such “induced” co- author links.  Induced Collaboration Repetition Count and Influence Gap Here also, all highly repeating “induced” collaborations have small “influence” gap AI Domain Networking Domain

27 Networking Domain: 1. Giant component size 8152, Second Largest Component size 63 2. 28% (167) of induced collaboration links took part in the merging process AI Domain: 1. Giant component size 16203, Second Largest Component size 41 2. 36:6% (263) of induced collaboration links took part in the merging process

28  Interactions during conferences can be used as a tool to boost own citation-count.  This can indirectly help in creating effective future collaborations and this cycle goes on.  With time people are being more and more aware about the benefits of interacting with fellow researchers during conferences. Need to check 1. Influence of specific fields of interacting authors on creation of “induced” citations 2. Effects of “induced” citations/collaborations on the citation/collaboration degree distribution 3. Modeling the dynamics

29  1. A. L. Barabasi, H. Jeong, Z. Neda, E. Ravasz, A. Schubert, and T. Vicsek: “Evolution of the social network of scientic collaborations”. Physica A: Statistical Mechanics and its Applications, 311(3-4):590 - 614, 2002.  2. A. Chin and M. Chignell.: “A social hypertext model for finding community in blogs. In HYPERTEXT '06”. Proceedings of the seventeenth conference on Hypertext and hypermedia, pages 11-22, New York, NY, USA, 2006. ACM Press.  3. Q. He, B. Chen, J. Pei, B. Qiu, P. Mitra, and C. L. Giles: “Detecting topic evolution in scientific literature: how can citations help?” In CIKM, pages 957-966, 2009.  4. X. Liu, J. Bollen, M. L. Nelson, and H. Van de Sompel.: “Co-authorship networks in the digital library research community”. Information processing & management, 41(6):1462-1480, 2005.  5. P. Divakarmurthy, P. Biswas, and R. Menezes.: “A temporal analysis of geographical distances in computer science collaborations”. In SocialCom/PASSAT, pages 657-660. IEEE, 2011.


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