MO3.R4.3

On the Lossy Compression of Spatial Networks

Praneeth Kumar Vippathalla, Martin Wachiye Wafula, Mihai-Alin Badiu, Justin P. Coon, University of Oxford, United Kingdom
Session:
Rate Distortion Theory 1
Track:
9: Shannon Theory
Location:
Omikron II
Presentation Time:
Mon, 8 Jul, 15:15 - 15:35
Session Chair:
Wenyi Zhang,
Abstract
In this paper, we address the lossy compression of spatial networks, namely random geometric graphs, where two nodes are connected by an edge with a probability that depends on the distance between the nodes. We carry out this study by considering the nth order information-distortion function, which quantifies the complexity of a random graph under a distortion criterion. Our main result is a partial characterization of the information-distortion function for a random geometric graph with the Hamming distortion measure.
Session MO3.R4
MO3.R4.1: On the Rate-Distortion Function for Sampled Cyclostationary Gaussian Processes with Memory
Zikun Tan, Ron Dabora, Ben-Gurion University of the Negev, Israel; H. Vincent Poor, Princeton University, United States
MO3.R4.2: On Convergence of Discrete Schemes for Computing the Rate-Distortion Function of Continuous Source
Lingyi Chen, Shitong Wu, Tsinghua University, China; Wenyi Zhang, University of Science and Technology of China, China; Huihui Wu, University of Electronic Science and Technology of China, China; Hao Wu, Tsinghua University, China
MO3.R4.3: On the Lossy Compression of Spatial Networks
Praneeth Kumar Vippathalla, Martin Wachiye Wafula, Mihai-Alin Badiu, Justin P. Coon, University of Oxford, United Kingdom
MO3.R4.4: Estimation of Rate-Distortion Function for Computing with Decoder Side Information
Heasung Kim, Hyeji Kim, Gustavo De Veciana, The University of Texas at Austin, United States
Resources
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