Arboricity and subgraph listing algorithms
SIAM Journal on Computing
Stereo Correspondence Through Feature Grouping and Maximal Cliques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Planar orientations with low out-degree and compaction of adjacency matrices
Theoretical Computer Science
The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
An Analysis of Some Graph Theoretical Cluster Techniques
Journal of the ACM (JACM)
Enumerating all connected maximal common subgraphs in two graphs
Theoretical Computer Science
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
The worst-case time complexity for generating all maximal cliques and computational experiments
Theoretical Computer Science - Computing and combinatorics
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Note: A note on the problem of reporting maximal cliques
Theoretical Computer Science
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
The tidy set: a minimal simplicial set for computing homology of clique complexes
Proceedings of the twenty-sixth annual symposium on Computational geometry
Bounded arboricity to determine the local structure of sparse graphs
WG'06 Proceedings of the 32nd international conference on Graph-Theoretic Concepts in Computer Science
External-memory network analysis algorithms for naturally sparse graphs
ESA'11 Proceedings of the 19th European conference on Algorithms
Fast algorithms for maximal clique enumeration with limited memory
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding top-n colossal patterns based on clique search with dynamic update of graph
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Maximal cliques based rigid body motion segmentation with a RGB-D camera
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Maximal clique enumeration for large graphs on hadoop framework
Proceedings of the first workshop on Parallel programming for analytics applications
Fast Circular Arc Segmentation Based on Approximate Circularity and Cuboid Graph
Journal of Mathematical Imaging and Vision
Towards topological analysis of high-dimensional feature spaces
Computer Vision and Image Understanding
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We implement a new algorithm for listing all maximal cliques in sparse graphs due to Eppstein, Löffler, and Strash (ISAAC 2010) and analyze its performance on a large corpus of real-world graphs. Our analysis shows that this algorithm is the first to offer a practical solution to listing all maximal cliques in large sparse graphs. All other theoretically-fast algorithms for sparse graphs have been shown to be significantly slower than the algorithm of Tomita et al. (Theoretical Computer Science, 2006) in practice. However, the algorithm of Tomita et al. uses an adjacency matrix, which requires too much space for large sparse graphs. Our new algorithm opens the door for fast analysis of large sparse graphs whose adjacency matrix will not fit into working memory.