2 research outputs found
Communication-Optimal Distributed Dynamic Graph Clustering
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
nodes that is observed at remote sites over time , the two
proposed algorithms have communication costs and
( hides a polylogarithmic factor), almost matching
their lower bounds, and , respectively, in the
message passing and the blackboard models. More importantly, we prove that at
each time point in 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.Comment: Accepted and to appear in AAAI'1