case study on network topology pdf

Network Topology to Predict Bibliometrics Indices: A Case Study

• Conference paper
• First Online: 20 November 2022
• Cite this conference paper

• Vincenza Carchiolo 11 ,
• Marco Grassia 11 ,
• Michele Malgeri 11 &
• Giuseppe Mangioni 11  

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13635))

Included in the following conference series:

• International Conference on Information Integration and Web

722 Accesses

Co-authorship networks have been widely studied in recent years, but today new techniques and increasing computational power permit performing novel analysis and evaluate larger datasets. One of the emerging topic is the investigation of the reasons that determine the success of some people among the others. Researchers and academic community are of interest because the metric to evaluate their performance, although widely debated, are consolidated and based on bibliometrics indices, that are quantifiable. Moreover, the paradigm of complex networks added another perspective that, often, allows discovering hidden behaviors. This paper proposes an analysis of four large datasets related to Italian academic working for public institutions, and grouped by law in academic disciplines, using network analysis tools in order to compare their structure and characteristics highlighting, if any, similarities and difference. Moreover, applying a machine learning approach, the authors try to predict some bibliometric indices using network topology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

The strength of co-authorship ties through different topological properties.

Correlation Between Researchers’ Centrality and H-Index: A Case Study

Constructing Bibliometric Networks from Spanish Doctoral Theses

The Italian Ministry of University and Research.

Elsevier Developer - Academic Research. Accessed July 2021

Google Scholar  

Elsevier Developer - API Service Agreement. Accessed July 2021

Ministero dell’Universitá e della Ricerca - Professori e Ricercatori. Accessed May 2021

Scopus fact sheet. www.elsevier.com/__data/assets/pdf_file/0017/114533/Scopus-fact-sheet-2022_WEB.pdf . Accessed April 2022

Barabási, A., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica A: Stat Mech Appl 311 (3), 590–614 (2002). www.sciencedirect.com/science/article/pii/S0378437102007367

Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25 (2), 163–177 (2001). https://doi.org/10.1080/0022250X.2001.9990249

Article   MATH   Google Scholar  

Carchiolo, V., Grassia, M., Malgeri, M., Mangioni, G.: Analysis of the co-authorship sub-networks of Italian academic researchers. Stud. Comput. Intell. 1015 , 321–327 (2022)

Article   Google Scholar  

Carchiolo, V., Grassia, M., Malgeri, M., Mangioni, G.: Co-authorship networks analysis to discover collaboration patterns among Italian researcher. Future Internet 14 (2022)

Carchiolo, V., Grassia, M., Malgeri, M., Mangioni, G.: Correlation between researchers’ centrality and h-index: a case study. In: Intelligent Distributed Computing XV. Springer, Heidelberg (2022)

Carchiolo, V., Grassia, M., Malgeri, M., Mangioni, G.: Preliminary characterization of Italian academic scholars by their bibliometrics. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds.) Intelligent Distributed Computing XIV, pp. 343–354. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96627-0_31

Chapter   Google Scholar  

Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G.: Communities unfolding in multislice networks. In: da F. Costa, L., Evsukoff, A., Mangioni, G., Menezes, R. (eds.) CompleNet 2010. CCIS, vol. 116, pp. 187–195. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25501-4_19

Chapter   MATH   Google Scholar  

Clauset, A., Larremore, D.B., Sinatra, R.: Data-driven predictions in the science of science. Science 355 (6324), 477–480 (2017)

Fortunato, S., et al.: Science of science. Science 359 (6379) (2018)

Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. 102 (46), 16569–16572 (2005)

Molontay, R., Nagy, M.: Two decades of network science: as seen through the co-authorship network of network scientists. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019, pp. 578–583. Association for Computing Machinery, New York (2019)

Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74 , 036104 (2006)

Article   MathSciNet   Google Scholar  

Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69 , 026113 (2004)

Nicosia, V., Mangioni, G., Carchiolo, V., Malgeri, M.: Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech. Theory Exp. 2009 (03), P03024 (2009)

Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Social Netw. 32 (3), 245–251 (2010). www.sciencedirect.com/science/article/pii/S0378873310000183

Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. In: WWW 1999 (1999)

Peixoto, T.P.: Efficient monte carlo and greedy heuristic for the inference of stochastic block models. Phys. Rev. E 89 , 012804 (2014)

Vespignani, A.: Twenty years of network science (2018)

Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393 (6684), 440–442 (1998)

Zeng, A., et al.: The science of science: from the perspective of complex systems. Phys. Rep. 714 , 1–73 (2017)

Article   MathSciNet   MATH   Google Scholar  

Download references

Acknowledgment

This work has been partially supported by the project of University of Catania PIACERI, PIAno di inCEntivi per la Ricerca di Ateneo .

Author information

Authors and affiliations.

Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, Università degli Studi di Catania, Catania, Italy

Vincenza Carchiolo, Marco Grassia, Michele Malgeri & Giuseppe Mangioni

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Vincenza Carchiolo .

Editor information

Editors and affiliations.

La Trobe University, Melbourne, VIC, Australia

Eric Pardede

Monash University, Melbourne, VIC, Australia

Pari Delir Haghighi

Johannes Kepler University Linz, Linz, Austria

Ismail Khalil

Gabriele Kotsis

Figure  6 shows a bivariate analysis of each centrality measures respect to H-index, Fig.  7 and 8 show a bivariate analysis of document-count and citation-count. In the figures, the cyan refers to MAT/05 , yellow to INF/01 , green to ING-INF/05 , and, finally, orange to SECS-P/01 .

Centrality measure vs. H-index

Centrality measure vs. Document-count

Centrality measure vs. Citation-count

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Cite this paper.

Carchiolo, V., Grassia, M., Malgeri, M., Mangioni, G. (2022). Network Topology to Predict Bibliometrics Indices: A Case Study. In: Pardede, E., Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Information Integration and Web Intelligence. iiWAS 2022. Lecture Notes in Computer Science, vol 13635. Springer, Cham. https://doi.org/10.1007/978-3-031-21047-1_16

Download citation

DOI : https://doi.org/10.1007/978-3-031-21047-1_16

Published : 20 November 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-21046-4

Online ISBN : 978-3-031-21047-1

eBook Packages : Computer Science Computer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Download Free PDF

The Dynamic of Banking Network Topology, Case Study: Indonesian Presidential Election Event

2018, The 2018 International Workshop on Big Data & Information Security (IWBIS)

Information and communication technologies have brought major changes in data storage and processing. Various types and high volume of data has been digitalized and support mining-based data processing to provide knowledge in a modern and efficient way. Banking transaction data has been stored digitally and suitable for the mining process especially in network science model. Understanding transaction system risk requires fundamental study on payments flow and bank behavior in various situations. Lehman Brother’s failure spread contagion impact in a short time indicates that financial markets have interdependent properties and connected to each other in a large network. Thus, overall system network approach becomes more important than a single bank. Political conditions greatly affect economic stability including the banking and financial sectors. Presidential election is a major political event for a nation. This affected on community sentiment and financial market. However, the linkage between political events and topological changes is poorly understood. This research presents an insight of the event driven dynamic network topology with banking transaction as a case study. We search for the banking transaction network topology dynamic driven by 2014 Indonesian presidential election event. We discover that banks are more engaged to others in larger value 3 days before the end of campaign period and less engaged to others in smaller value in the end of campaign period. Unique transaction activity between banks remain stable with low declination in the end of campaign period. This scenario provides the possibility to learn the banking transaction pattern and support the financial system stability supervision.

Related papers

Journal of Theoretical and Applied Information Technology, 2020

The instability of financial system issues might trigger a bank failure, evoke spillovers, and generate contagion effects which negatively impacted the financial system, ultimately on the economy. This phenomenon is the result of the highly interconnected banking transaction. The banking transactions network is considered as a financial architecture backbone. The strong interconnectedness between banks escalates contagion disruption spreading over the banking network and trigger the entire system collapse. This far, the financial instability is generally detected using macro approach mainly the uncontrolled transaction deficits amount and unpaid foreign debt. This research proposes financial instability detection in another point of view, through the macro view where the banking network structure are explored globally and micro view where focuses on the detailed network patterns called motif. Network triadic motif patterns used as a denomination to detect financial instability. The most related network triadic motif changes related to the instability period are determined as detector. We explore the banking network behavior under financial instability phenomenon along with the major religious event in Indonesia, Eid al-Fitr. We discover one motif pattern as the financial instability underlying detector. This research help to support the financial system stability supervision.

Financial Innovation

To examine the interdependency and evolution of Pakistan’s stock market, we consider the cross-correlation coefficients of daily stock returns belonging to the blue chip Karachi stock exchange (KSE-100) index. Using the minimum spanning tree network-based method, we extend the financial network literature by examining the topological properties of the network and generating six minimum spanning tree networks around three general elections in Pakistan. Our results reveal a star-like structure after the general elections of 2018 and before those in 2008, and a tree-like structure otherwise. We also highlight key nodes, the presence of different clusters, and compare the differences between the three elections. Additionally, the sectorial centrality measures reveal economic expansion in three industrial sectors—cement, oil and gas, and fertilizers. Moreover, a strong overall intermediary role of the fertilizer sector is observed. The results indicate a structural change in the stock ma...

2019 International Workshop on Big Data and Information Security (IWBIS)

Banking transactions are the fundamental indicator of an economy. The complex, large-scale, and interconnected transactions between banks are considered as a financial architecture backbone. Interbank transactions are modelled into a banking network. The network strong interconnectedness escalates contagion default spreading over the network and trigger the entire system to collapse. This far, financial crisis is detected through the macro approach. This study seek for another point of view. We predict the financial crisis through the micro view, where focuses on detailed network components called motif. Network motifs are used as a tool to predict financial crisis by focusing on the microscopic structure change within a network. The network structure changes due to a disruption. We explore the network motifs change under pre-crisis, crisis, and post-crisis periods (October 2007 to March 2009). We use the network structural change response to determine predictor(s). The motif types that having major changes along the crisis period then determined as crisis predictor. Through the scenario, we discover one motif as the underlying predictor of financial crisis. This study is able to support the financial system stability supervision.

ArXiv, 2021

We construct a network of 1.6 million nodes from banking transactions of users of Rabobank. We assign two weights on each edge, which are the aggregate transferred amount and the total number of transactions between the users from the year 2010 to 2020. We present a detailed analysis of the unweighted and both weighted networks by examining their degree, strength, and weight distributions, as well as the topological assortativity and weighted assortativity, clustering, and weighted clustering, together with correlations between these quantities. We further study the meso-scale properties of the networks and compare them to a randomized reference system. We also analyze the characteristics of nodes and edges using centrality measures to understand their roles in the money transaction system. This will be the first publicly shared dataset of intra-bank transactions, and this work highlights the unique characteristics of banking transaction networks with other scalefree networks.

Economies, 2022

In 2008, the Lehman Brothers’ bankruptcy, accumulated from the global financial crisis, proved a unique role of the highly interconnected financial entities. Shocks in a bank might trigger loss, induce spillovers, provoke a contagion shock spreading to other entities, trigger the whole banking system to collapse, and ultimately unsettle the worldwide economy. Therefore, evaluating financial stability through a system-wide network approach provides more adequate knowledge than evaluating a bank as an individual. In this approach, individual banks and their transaction activities are modeled into a transaction network, forming a network topology. Financial shocks are generally detected through various macro procedures, such as outstanding external debt and uncontrolled transaction deficits. This study proposes financial shock detection from a macro and micro perspective by exploring the effect of disruption on transaction network structure. We investigate the most changing triadic mot...

One of the most important features of capital markets as an adaptive complex networks is their collective behavior. In this paper, we have analyzed the banking sectors of 4 world stock markets, which composed of emerging and matures ones. By applying one the important complexity notions, Random matrix theory(RMT), it is founded that mature markets have a higher degree of collective<br> behavior, even though we used RMT tools: participation ratio(PR), node participation ratio(NPR)and relative participation ratio(RPR) , which NPR illustrated independent banks than whole market and RPR compared collective behavior of markets by a normal range. By applying local and global perturbations, we concluded that mature markets are more vulnerable to perturbations due to the high level of collective behavior. Finally, by drawing the dendrograms and heat maps of the correlation matrices, we reaffirmed the stronger cross-correlation in the mature markets.

The Financial Crisis of 2007-2008 was a very complex and impactful global event. The goal of this research is to explore the possibility of using a bank’s social relations to estimate a bank’s financial strength. We apply Natural Language Processing techniques to a corpus of financial data released by the NLP Unshared Task in PoliInformatics in 2014 in order to explore and better understand this possibility. Our work begins with the extraction of named entities from the corpus to establish names of people involved in the crisis. We then aggregate the social histories of these individuals from an online collaborative knowledge base: Freebase. Accordingly, we use the social histories of entities to establish social connections between them. We end with a visualization of the connections we found: a presentation of a social financial crisis network.

Journal of Economic Dynamics and Control, 2017

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

Scientific Reports, 2013

European Central Bank Working Paper, 2022

SSRN Electronic Journal, 2000

Advances in Complex Systems, 2011

Business, Management and Education

Computational Management Science, 2013

Network Science, 2015

Handbook of Computational Economics, 2018

The Journal of Financial Data Science, 2019

Computational Methods in Financial Engineering, 2008

Quality & Quantity, 2013

Frontiers in Physics, 2021

Computational Statistics & Data Analysis, 2005

Universal Journal of Accounting and Finance, 2019

International Journal of Finance & Economics, 2019

Iconic Research and Engineering Journals, 2019

IEEE Transactions on Emerging Topics in Computational Intelligence, 2018

SSRN Electronic Journal, 2016

Related topics

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024