Communication-Optimal Distributed Dynamic Graph Clustering.

ABSTRACT: We consider the problem of clustering graph nodes over large-scale dynamic graphs, such as citation networks, images and web networks, when graph updates such as node/edge insertions/deletions are observed distributively. We propose communication-efficient algorithms for two well-established communication models namely the message passing and the blackboard models. Given a graph with n nodes that is observed at s remote sites over time [1, t], the two proposed algorithms have communication costs Õ(ns) and Õ(n + s) (Õ hides a polylogarithmic factor), almost matching their lower bounds, Ω(ns) and Ω (n + s), respectively, in the message passing and the blackboard models. More importantly, we prove that at each

SUBMITTER: Zhu CJ 

PROVIDER: S-EPMC9275443 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature