Geo-Awareness of Learnt Citations Prediction for
Scientific Publications (Demo Paper)
Ce Li Will Postler Ian Johnson Paul Brinkman
Evan Gossling Bailey Gorlewski Goce Trajcevski
Iowa State University,Ames, IA, USA

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Hamburg, DE

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Outline• Background/Motivation• System/Development• Overview• Conclusions
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Background(What are Information
Cascades?)Social Media Study

Market study
experiment3

Background
Domain:- user-generated content (e.g., microblogs)- online business (e.g., viral marketing and advertising)-…
Goal:- Explore and exploit the “trajectories” and structures of the evolution of information cascades

In this work:
Domain: Scientific Literature-…
Goal: Quantifying and predicting the long-term impact of scientific papers (and individual authors)- Important implications for many academic policy decisions (identifying emerging trends; assessing the merits ofproposals for potential funding).
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Motivation:●

Develop a system which, for a particular publication, would combine displaying:○

Citation progress○Map visualization■●Basic “demographics”.

Intended Users○

Individual Scientists○Funding Institutions○

University Administrators

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ML Foundations- Jared David Tadeo Guerrero-Sosa, Víctor Hugo Menéndez-Domínguez, María-Enriqueta
Castellanos-Bolaños, and Luis Fernando Curi Quintal. Use of Graph Theory for theRepresentation of Scientific Collaboration. In ICCCI, 2019.
- Song Jiang, Bernard Koch, and Yizhou Sun. 2021. HINTS: Citation time series prediction for newpublications via dynamic heterogeneous information network embedding. In WWW, 2021.
- Xovee Du, Ting Zhong, Ce Li, Goce Trajcevski, Fan Zhou. Heterogeneous Dynamical Network
for Learning Scientific Impact Propagation. Knowledge Based Systems, 238, 2022.

DatasetAPS (American Physical Society)- 616K papers- 17 journals- 1893-2017
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Broad set of tasks●

Design Infrastructure●

Initialize Database with dataset●

Implement UI Design●

Create Queries●

Implement backend ML logic●

Data Visualization●

CI/CD Pipeline
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Software Architecture●Main modules○Frontend○Backend○Servers●Interfacing●"Additional software”
Final Report.docx (iastate.edu)

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Standards●●●●IEEE 830 - Software Requirements Specifications
IEEE 1016 - Software Design Descriptions
IEEE 12207 - Software Life Cycle Processes
IEEE 1028 - Software Reviews and Audits

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9Tools
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PythonFlask
HTML/Javascript/CSS
JS Libraries (jQuery, Ajax)
MapboxMySQLDockerCI/CD
Information Cascade Models

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Testing●Pytest unit testing●Interface testing●

Integration testing●Regression testing●Acceptance testing●System testing

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Quick “Look-and-feel”

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Future Work & Extensions●User supplied datasets○○

Currently only APS
Training of models based on additional datasets●

New models/algorithms (VaCas, CasCN, etc.)●

More filtering options●○

Filtering based on region, general topics, etc.

○Query for n-th most prolific paper, 10 best papers (based on citations), etc.

Additional queries

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Thank you!
Questions(?)

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