Communication-Optimal Distributed Dynamic Graph Clustering
Abstract
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Modern networks are of huge sizes as well as high dynamics, which challenges the efficiency of community detection algorithms. In this paper, we study the problem of overlapping community detection on distributed and dynamic graphs. Given a distributed, undirected and unweighted
Clustering is an established tool for the analysis of networks or network-like data. The partitioning of the graph of a network into so-called clusters is meant to yield insights into its function, and to reveal common properties amongst nodes, as well as properties of individual
Clustering large datasets is a fundamental problem with a number of applications in machine learning. Data is often collected on different sites and clustering needs to be performed in a distributed manner with low communication. We would like the quality of the clustering in the
Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied Planted Bisection
Algorithms or target functions for graph clustering rarely admit quality guarantees or optimal results in general. Based on properties of minimum-cut trees, a clustering algorithm by Flake et al. does however yield such a provable guarantee, which ensures the quality of bottlenec
Search Pointers organize the main part of the application on the Internet. However, because of Information management hardware, high volume of data and word similarities in different fields the most answers to the user s’ questions aren`t correct. So the web graph clustering and