Correlation mining has been widely studied due to its ability for discovering the underlying occurrence dependency between objects. However, correlation mining in graph databases ...
A Bayesian treatment of latent directed graph structure for non-iid data is provided where each child datum is sampled with a directed conditional dependence on a single unknown p...
Existing graph mining algorithms typically assume that databases are relatively static and can fit into the main memory. Mining of subgraphs in a dynamic environment is currently ...
A random graph model based on Kronecker products of probability matrices has been recently proposed as a generative model for large-scale real-world networks such as the web. This...
The paper describes a novel technique to visualize graphs with extended node and link labels. The lengths of these labels range from a short phrase to a full sentence to an entire...
Pak Chung Wong, Patrick Mackey, Ken Perrine, James...