Federated Myopic Community Detection with One-shot Communication

Chuyang Ke, Jean Honorio
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:4937-4954, 2022.

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. In addition, our experiments show that in an extremely sparse network with 10000 nodes, our method can achieve exact recovery of the community structure even if every client has access to only 20 nodes. 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-ke22a, title = { Federated Myopic Community Detection with One-shot Communication }, author = {Ke, Chuyang and Honorio, Jean}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {4937--4954}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/ke22a/ke22a.pdf}, url = {https://proceedings.mlr.press/v151/ke22a.html}, 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. In addition, our experiments show that in an extremely sparse network with 10000 nodes, our method can achieve exact recovery of the community structure even if every client has access to only 20 nodes. 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. } }
Endnote
%0 Conference Paper %T Federated Myopic Community Detection with One-shot Communication %A Chuyang Ke %A Jean Honorio %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-ke22a %I PMLR %P 4937--4954 %U https://proceedings.mlr.press/v151/ke22a.html %V 151 %X 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. In addition, our experiments show that in an extremely sparse network with 10000 nodes, our method can achieve exact recovery of the community structure even if every client has access to only 20 nodes. 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.
APA
Ke, C. & Honorio, J.. (2022). Federated Myopic Community Detection with One-shot Communication . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:4937-4954 Available from https://proceedings.mlr.press/v151/ke22a.html.

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