Data-Driven Analysis: Decomposition-Based Approach for Multilayer Networks (MLNs)

Speaker:  Sharma Chakravarthy – Bedford, TX, United States
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science


We are on the cusp of holistically analyzing a variety of data being collected in every walk of life in diverse ways. For this, current analytics and science are being extended (Big Data Analytics/Science) along with new approaches to benefit humanity at large in the best possible way. This warrants developing and/or using new approaches -- technological, scientific, and systems -- in addition to building upon and integrating with the ones that have been developed so far. With this ambitious goal, there is also the risk of these advancements being misused or abused as we have seen so many times with respect to new technologies.

In the first part of the presentation, we take the audience on a retrospective stroll on the approaches that have come about for managing and analyzing data over the last 40+ years. Since the advent of Database Management Systems (or DBMSs) and especially the Relational DBMSs (or RDBMSs), data management and analysis have seen several significant strides. Today, data has become an important tool (or even a weapon) in society and its role and importance is unprecedented.

Modeling and analysis of complex data sets (i.e., data sets with multiple entity and feature types along with their relationships) are challenging especially if one wants to do it in a flexible and efficient way to match the analysis objectives. Analysis entails understanding of the data set with respect to entity and feature combinations as well as inferring actionable knowledge. We posit that modeling of these data sets is equally important and can be done elegantly using multilayer networks (or multiplexes -- layers of networks that are inter-connected) instead of using a single graph. Recent research is towards efficient and scalable approaches for this representation.

In second part of this talk, we first illustrate the elegance of multilayer networks for modeling by using a few well-known data sets. Flexibility of analysis comes from this approach to modeling. Then we discuss the decomposition approach for computation which is efficient in that it combines communities (and hubs) from individual layers to form loss-less communities for any combination of layers in a multilayer network. Finally, we present several diverse case studies to showcase the approach.

Sharma Chakravarthy is a co-author of the book “Stream Data Processing: A Quality of Service Perspective”, Springer-Verlag, April 2009 (ISBN: 978-0-387-71002-0).

About this Lecture

Number of Slides:  120
Duration:  136 minutes
Languages Available:  English
Last Updated: 

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