We use cluster analysis as a unifying principle for problems from low, middle and high level vision. The clustering problem is viewed as graph partitioning, where nodes represent ...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use this representation to design an efficient algorithm for computing the largest...
We present the architecture of an end-to-end semantic search engine that uses a graph data model to enable interactive query answering over structured and interlinked data collecte...
In this paper, we introduce a new method, GraphScape, to visualize multivariate networks, i.e., graphs with multivariate data associated with their nodes. GraphScape adopts a land...
Kai Xu 0003, Andrew Cunningham, Seok-Hee Hong, Bru...
State-of-the-art techniques for probability sampling of users of online social networks (OSNs) are based on random walks on a single social relation. While powerful, these methods ...
Minas Gjoka, Carter T. Butts, Maciej Kurant, Athin...