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International Conference on Big Data Analytics

BDA 2019: Big Data Analytics pp 227–244Cite as

Why Multilayer Networks Instead of Simple Graphs? Modeling Effectiveness and Analysis Flexibility and Efficiency!

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11932))

Abstract

We are on the cusp of analyzing a variety of data being collected in every walk of life - social, biological, health-care, corporate, climate, to name a few. We are also in search for models and analytical techniques that can accommodate more complex and increasingly large size data (scalability). Our ability to analyze large complex, disparate data for a broad set of analysis objectives differentiates big data analytics from mining which is narrow in scope. Hence, flexibility of analysis (different from scalability) is important. Concomitantly, efficiency is important due to large number of analysis needs. Our ultimate goal is to go from vertical analysis of data individually (corresponding to one of the 4 V’s) to holistically (also termed fusion-based) analyze that corresponds to all or a subset of V’s!

In order to accomplish the above, we are always in search for more effective models to represent data and different analysis techniques that support flexibility of analysis, efficiency, and scalability. We want to use techniques that have worked well – whether it is for modeling, efficiency or scalability. We also want to extend these techniques and/or develop new and improved ones to accommodate more complex, diverse, and larger size data.

The goal of this paper is to provide the reader an understanding of data analysis approaches using graphs. Our thesis is that there are several ways in which a graph representation can be used – both for modeling and analysis. We will take the reader through the evolution of graph usage and relevance leading to the current state of the use of multilayer Networks (MLNs) or multiplexes for modeling and analysis. Graphs are not new, but how they are used for big data analytics is going through a transformation which is important to understand. The hope is that the reader understands the path that has led us to this juncture and how graph usage is extended!

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Notes

  1. 1.

    Other aggregation approaches have the same problem.

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Acknowledgment

We would like to thank Dr. Sanjukta Bhowmick on her collaboration with us on the multilayer network analysis.

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Authors and Affiliations

  1. Information Technology Laboratory (IT Lab), Computer Science and Engineering Department, University of Texas at Arlington, Arlington, TX, 76019, USA

    Sharma Chakravarthy, Abhishek Santra & Kanthi Sannappa Komar

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  1. Sharma Chakravarthy
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  2. Abhishek Santra
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  3. Kanthi Sannappa Komar
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Correspondence to Sharma Chakravarthy .

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Editors and Affiliations

  1. Missouri University of Science and Technology, Rolla, MO, USA

    Sanjay Madria

  2. Harbin Institute of Technology, Shenzhen, China

    Philippe Fournier-Viger

  3. Ahmedabad University, Ahmedabad, India

    Sanjay Chaudhary

  4. International Institute of Information Technology, Hyderabad, India

    P. Krishna Reddy

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Chakravarthy, S., Santra, A., Komar, K.S. (2019). Why Multilayer Networks Instead of Simple Graphs? Modeling Effectiveness and Analysis Flexibility and Efficiency!. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P. (eds) Big Data Analytics. BDA 2019. Lecture Notes in Computer Science(), vol 11932. Springer, Cham. https://doi.org/10.1007/978-3-030-37188-3_14

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