Year: 2012
Type: preprint
Abstract: The maximum clique problem is a well known NP-Hard problem with applications in data mining, network analysis, informatics, and many other areas. Although there exist several algorithms with acceptable runtimes for certain classes of graphs, many of them are infeasible for massive graphs. We present a new exact algorithm that employs novel pruning techniques to very quickly find maximum cliques in large sparse graphs. Extensive experiments on several types of synthetic and real-world graphs show that our new algorithm is up to several orders of magnitude faster than existing algorithms for most instances. We also present a heuristic variant that runs orders of magnitude faster than the exact algorithm, while providing optimal or near-optimal solutions.
Source: arXiv (Cornell University)
Institutions: Northwestern University, Purdue University West Lafayette
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: AF:Small: Combinatorial Algorithms to Enable Derivative Computations on Multicore Architectures, G1418651512, G222768804, G2240970103, Collaborative Research: An Application Driven I/O Optimization Approach for PetaScale Systems and Scientific Discoveries, ACTIVE STORAGE WITH ANALYTICS CAPABILITIES AND I/0 RUNTIME SYSTEM FOR PETASCALE SYSTEMS, G3060264110, EAGER: Discovering Knowledge from Scientific Research Networks, Collaborative Research: Scalable I/O Middleware and File System Optimizations for High-Performance Computing, G4316568219, G4915832969, SDCI HPC: Improvement: Parallel I/O Software Infrastructure for Petascale Systems, DC: Medium: Collaborative Research: ELLF: Extensible Language and Library Frameworks for Scalable and Efficient Data-Intensive Applications, G6004917201, G6092407642, G6337171508, Collaborative Research: Understanding Climate Change: A Data Driven Approach, Collaborative Research: CT-M: Hardware Containers for Software Components - Detection and Recovery at the Hardware/Software Interface, G7251410384, Collaborative Research: Advanced Compiler Optimizations and Programming Language Enhancements for Petascale I/O and Storage, G7748126781, Data- and Analytics Driven Fault-tolerance and Resiliency Strategies for Peta-Scale Systems, Travel Support for Workshop: Reaching Exascale in this Decade to be Co-Located with International Conference on High-Performance Computing (HiPC 2010), G8642469239, G8734057514, G997983094
OpenAlex ID: W2951947541