Communication-Optimal Distributed Dynamic Graph Clustering [article]

Chun Jiang Zhu, Tan Zhu, Kam-Yiu Lam, Song Han, Jinbo Bi
2018 arXiv   pre-print
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
more » ... 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 time point in [1,t] our algorithms generate clustering quality nearly as good as that of centralizing all updates up to that time and then applying a standard centralized clustering algorithm. We conducted extensive experiments on both synthetic and real-life datasets which confirmed the communication efficiency of our approach over baseline algorithms while achieving comparable clustering results.
arXiv:1811.06072v1 fatcat:5w6oqvitjzas5jdlj3hklols5a