Open-access article

Communication-Optimal Distributed Dynamic Graph Clustering

Abstract

No abstract recorded for this item.

Similar items

  • On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs

    Scholarly record2018CoRRXun Jian, Xiang Lian, Lei Chen

    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

  • The Dynamic Graph Clustering Problem - ILP-Based Approaches Balancing Optimality and the Mental Map

    Scholarly record2008Florian Hübner

    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

  • Communication-Optimal Distributed Clustering

    Scholarly record2016NIPSJiecao Chen, He Sun, David P. Woodruff, Qin Zhang

    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

  • Distributed Community Detection in Dynamic Graphs - (Extended Abstract)

    Scholarly record2013SIROCCOAndrea E. F. Clementi, Miriam Di Ianni, Giorgio Gambosi, Emanuele Natale

    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

  • Dynamic Graph Clustering Using Minimum-Cut Trees

    Scholarly record2009J. Graph Algorithms Appl.Robert Görke, Tanja Hartmann, Dorothea Wagner

    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

  • Finding community base on web graph clustering

    Scholarly record2014Alireza Rezaee

    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