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16.10 - Interactive Visual Exploration of Large Graphs with Enhanced Sampling and Summarization

Project - Summary

Many real-world systems are so large that capturing them entirely, analyzing them to extract information, and visualizing them for decision making are resource-consuming and challenging tasks. It is necessary to develop sampling and summarization approaches and integrate them with visualization interfaces to study large-scale graphs to understand the underlying real-world systems. We have designed and implemented a new similarity metric for use in graph summarization, allowing large graphs to be rendered more easily. The cross-platform, touch-based interface in Figure 1 illustrates the summarization hierarchy created by our metric with a real-world dataset and allows users to control the level of summarization.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Christoph Borst PI Not available Not available UL Lafayette
Mehmet Engin Tozal PI Not available Not available UL Lafayette
Nicholas Lipari Graduate Student Not available Not available UL Lafayette
Md Enamul Haque Graduate Student Not available Not available UL Lafayette

Project - Impact and Uses/Benefits

Complex networks have been studied across different fields of science such as mathematical, scientific, and engineering perspectives. For example, a citation network [15] is described as a set of academic papers where a connection represents one paper referencing another. In studying human disease propagation, researchers are interested in finding the correlation between diseases that are often diagnosed together. Diagnoses can form a complex network with diseases connected based on the frequency of co-occurrence in a patient population. Another common example of complex networks can be the World Wide Web, the largest network humanity has ever built. In this network the nodes are documents and the links are the uniform resource locators (URLs) embedded within the documents.

The proper visualization and monitoring of business processes is crucial for any enterprise. By using weighted summarization, we can provide an integrated model of the overall process, reduce the size of the dataset, observe connectivity and relationships among business processes, and identify what methods are suitable for analysis. The creation of graph summaries allows more efficient community detection within a variety of networks, such as product-consumer network and online social networks.

For companies and business, it is crucial to retain customers, build loyal relationships, and thereby avoid customer acquisition costs. Having a graph of customers and modeling their behavior helps us tho have insight of the data and maximize customer loyalty, retention and lifetime value. Weighted summarization can assist in the analysis and prediction of a new product and its impact on customers and help us to find the core cluster of customers. By understanding the different clusters of customers, we can observe the patterns of firmographic, demographic, and behavioral trends that correlate strongly with high-volume, modestly valuable, and borderline churn customers.

Due to the dynamics of the economy, the marketplace and company decision making can never be captured fully beforehand. However, by using a summarized model, we can estimate potential revenue sources (e.g., assist advertising agencies in choosing which companies to approach). We can identify which revenue source to pursue, what value to offer, how to price the value, and what markets can bear that prices.

[15] J. Gehrke, P. Ginsparg and J. Kleinberg, “Overview of the 2003 KDD Cup,” ACM SIGKDD Explorations Newsletter, vol. 5, no. 2, pp. 149-151, 2003.

Project - Deep Dive

Project - Documents

FilenameFilesizeLast modified
16.10_year_5_poster.pptx3.5 MiB2019/08/22 13:21
16.10_year_5_presentation.pdf689.0 KiB2019/08/22 13:21
16.10_year_5_final_report.pdf1.8 MiB2019/08/22 10:47
16.10_fig1.jpg190.8 KiB2019/08/21 10:33
projects/year5/16.10.txt · Last modified: 2019/08/22 10:50 by sally.johnson