Incremental k-core decomposition: algorithms and evaluation
Sarıyüce, A. E.
The VLDB Journal
Association for Computing Machinery
425 - 447
Item Usage Stats
MetadataShow full item record
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.
KeywordsDense subgraph discovery
Incremental graph algorithms
Streaming graph algorithms
Maximal clique findings
Protein function prediction
Published Version (Please cite this version)http://dx.doi.org/10.1007/s00778-016-0423-8
Showing items related by title, author, creator and subject.
Akçay, H. G.; Aksoy, S. (Institute of Electrical and Electronics Engineers, 2008-07)The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classification. In this paper, we present novel methods ...
Tse, Savio S.H. (Springer, 2008-10)We study the online bicriteria load balancing problem in this paper. We choose a system of distributed homogeneous file servers located in a cluster as the scenario and propose two online approximate algorithms for balancing ...
Sengezer, N.; Karasan, E. (Springer, 2004)In this paper, we study the problem of placing limited number of wavelength converting nodes in a multi-fiber network with static traffic demands and propose a tabu search based heuristic algorithm. The objective of the ...