Related papers: Federated Myopic Community Detection with One-shot Communication

Federated Myopic Community Detection with One-shot Communication

URL: http://arxiv.org/abs/2106.07255v1
Date: Mon, 14 Jun 2021 09:17:00 GMT
Title: Federated Myopic Community Detection with One-shot Communication
Authors: Chuyang Ke, Jean Honorio
Abstract summary: We study the problem of recovering the community structure of a myopic network under federated learning. Under this paradigm, we have several clients, each of them having a myopic view, observing a small subgraph of the network. We provide an efficient algorithm, which computes a consensus signed weighted graph from clients evidence, and recovers the underlying network structure in the central server.
Score: 31.930675913174742
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In this paper, we study the problem of recovering the community structure of a network under federated myopic learning. Under this paradigm, we have several clients, each of them having a myopic view, i.e., observing a small subgraph of the network. Each client sends a censored evidence graph to a central server. We provide an efficient algorithm, which computes a consensus signed weighted graph from clients evidence, and recovers the underlying network structure in the central server. We analyze the topological structure conditions of the network, as well as the signal and noise levels of the clients that allow for recovery of the network structure. Our analysis shows that exact recovery is possible and can be achieved in polynomial time. We also provide information-theoretic limits for the central server to recover the network structure from any single client evidence. Finally, as a byproduct of our analysis, we provide a novel Cheeger-type inequality for general signed weighted graphs.

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