Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
On Updating Problems in Latent Semantic Indexing
SIAM Journal on Scientific Computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enumeration of cospectral graphs
European Journal of Combinatorics - Special issue on algebraic combinatorics: in memory of J.J. Seidel
ACM SIGKDD Explorations Newsletter
Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
SIAM Journal on Computing
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
The Journal of Machine Learning Research
A tutorial on spectral clustering
Statistics and Computing
Scalable graph clustering using stochastic flows: applications to community discovery
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast approximate spectral clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental spectral clustering by efficiently updating the eigen-system
Pattern Recognition
A Fast Incremental Spectral Clustering for Large Data Sets
PDCAT '11 Proceedings of the 2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies
Nyström approximations for scalable face recognition: a comparative study
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Time and space efficient spectral clustering via column sampling
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A novel incremental principal component analysis and its application for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computer Science Review
Hi-index | 0.01 |
Partitioning a graph into groups of vertices such that those within each group are more densely connected than vertices assigned to different groups, known as graph clustering, is often used to gain insight into the organisation of large scale networks and for visualisation purposes. Whereas a large number of dedicated techniques have been recently proposed for static graphs, the design of on-line graph clustering methods tailored for evolving networks is a challenging problem, and much less documented in the literature. Motivated by the broad variety of applications concerned, ranging from the study of biological networks to the analysis of networks of scientific references through the exploration of communications networks such as the World Wide Web, it is the main purpose of this paper to introduce a novel, computationally efficient, approach to graph clustering in the evolutionary context. Namely, the method promoted in this article can be viewed as an incremental eigenvalue solution for the spectral clustering method described by Ng et al. (2001) [25]. The incremental eigenvalue solution is a general technique for finding the approximate eigenvectors of a symmetric matrix given a change. As well as outlining the approach in detail, we present a theoretical bound on the quality of the approximate eigenvectors using perturbation theory. We then derive a novel spectral clustering algorithm called Incremental Approximate Spectral Clustering (IASC). The IASC algorithm is simple to implement and its efficacy is demonstrated on both synthetic and real datasets modelling the evolution of a HIV epidemic, a citation network and the purchase history graph of an e-commerce website.