Abstract: A method for analyzing, querying, and mining graph databases using subgraph and similarity querying. An index structure, known as a closure tree, is defined for topological summarization of a set of graphs. In addition, a significance model is created in which the graphs are transformed into histograms of primitive components. Finally, connected substructures or clusters, comprising paths or trees, are detected in networks found in the graph databases using a random walk technique and a repeated random walk technique.

Abstract: Methods are provided for buffering nodes of a hierarchical index (e.g., R-tree, bang file, hB-tree) during operations on multi-dimensional data represented by the index. The methods are particularly suited for query operations, and a different method may be more suitable for one pattern of queries than another. Where queries are distributed in a relatively uniform manner across the domain or dataspace of an index, a node-area buffering method is provided. In this method nodes are cached or buffered in order of their respective areas (e.g., their minimum bounding areas), and a node having a smaller area will be replaced in cache before a node having a larger area. When, however, queries are not uniformly distributed, then a least frequently accessed buffering technique may be applied. According to this method statistics are maintained concerning the frequency with which individual index nodes are accessed. Those accessed less frequently are replaced in cache before those accessed more frequently.