DIVIDE-AND-CONQUER TOMOGRAPHY FOR LARGE-SCALE NETWORKS

被引：0

作者：

Santos, Augusto ^{[1
]}

Matta, Vincenzo ^{[2
]}

Sayed, Ali H. ^{[1
]}

机构：

[1] Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland

[2] Univ Salerno, DIEM, Via Giovanni Paolo 2, I-84084 Fisciano, SA, Italy

来源：

2018 IEEE DATA SCIENCE WORKSHOP (DSW) | 2018年

关键词：

Network inference; local inference; big-data; large-scale networks; network tomography;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work considers the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. Observations from only a subset of the nodes are collected, and the information is used to infer their local connectivity (local tomography). Recent results establish that, under suitable conditions on the network model, local tomography is achievable with high probability as the network size scales to infinity [1, 2]. Motivated by these results, we explore the possibility of reconstructing a larger network via repeated application of the local tomography algorithm to smaller network portions. A divide-and-conquer strategy is developed and tested numerically on some illustrative examples.

引用

页码：170 / 174

页数：5

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