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Activity Number:
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413
- Network Analysis and Network-Based Modeling
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Type:
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Contributed
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Date/Time:
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Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #307024
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Presentation
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Title:
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Network Heterogeneity and Strength of Connections
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Author(s):
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Sandipan Roy* and Subhadeep Mukhopadhyay
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Companies:
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University of Bath and Temple University
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Keywords:
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Grafield;
Laplacian;
nonparametric;
strength of connection;
kernel function;
algorithm
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Abstract:
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Detecting strength of connection in a network is a fundamental problem in understanding the relationship among individuals. Often it is more important to understand how strongly the two individuals are connected rather than the mere presence/absence of the edge. This paper introduces a new concept of strength of connection in a network through a nonparameteric object called “Grafield”. “Grafield” is a piece-wise constant bi-variate kernel function that compactly represents the affinity or strength of ties (or interactions) between every pair of vertices in the graph. We estimate the “Grafield” function through a spectral analysis of the Laplacian matrix followed by a hard thresholding (Gavish & Donoho, 2014) of the singular values. Our estimation methodology is valid for asymmetric directed network also. As a by product we get an efficient procedure for edge probability matrix estimation as well. We validate our proposed approach with several synthetic experiments and compare with existing algorithms for edge probability matrix estimation. We also apply our proposed approach to three real datasets to explore the strength of connection in real networks.
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Authors who are presenting talks have a * after their name.
