Incremental k-core decomposition: algorithms and evaluation

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 o...

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Veröffentlicht in:The VLDB journal Jg. 25; H. 3; S. 425 - 447
Hauptverfasser: Sarıyüce, Ahmet Erdem, Gedik, Buğra, Jacques-Silva, Gabriela, Wu, Kun-Lung, Çatalyürek, Ümit V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2016
Schlagworte:
Computer Science
Database Management
Regular Paper
Dense subgraph discovery
Streaming graph algorithms
Incremental graph algorithms
Core
ISSN:1066-8888, 0949-877X
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Beschreibung
Zusammenfassung: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.
ISSN:1066-8888
0949-877X
DOI:10.1007/s00778-016-0423-8