The VLDB Journal

, Volume 25, Issue 3, pp 425–447

Incremental k-core decomposition: algorithms and evaluation

  • Ahmet Erdem Sarıyüce
  • Buğra Gedik
  • Gabriela Jacques-Silva
  • Kun-Lung Wu
  • Ümit V. Çatalyürek
Regular Paper

DOI: 10.1007/s00778-016-0423-8

Cite this article as:
Sarıyüce, A.E., Gedik, B., Jacques-Silva, G. et al. The VLDB Journal (2016) 25: 425. doi:10.1007/s00778-016-0423-8
  • 535 Downloads

Abstract

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. For a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.

Keywords

k-Core Streaming graph algorithms Dense subgraph discovery Incremental graph algorithms 

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Ahmet Erdem Sarıyüce
    • 1
  • Buğra Gedik
    • 2
  • Gabriela Jacques-Silva
    • 3
  • Kun-Lung Wu
    • 3
  • Ümit V. Çatalyürek
    • 4
  1. 1.Sandia National LabsLivermoreUSA
  2. 2.Bilkent UniversityAnkaraTurkey
  3. 3.IBM T.J. Watson Research CenterYorktown HeightsUSA
  4. 4.The Ohio State UniversityColumbusUSA

Personalised recommendations