Frequent subgraph mining from streams of linked graph structured data

Nowadays, high volumes of high-value data (e.g., semantic web data) can be generated and published at a high velocity. A collection of these data can be viewed as a big, interlinked, dynamic graph structure of linked resources. Embedded in them are implicit, previously unknown, and potentially use- ful knowledge. Hence, eficient knowledge discovery algo- rithms for mining frequent subgraphs from these dynamic, streaming graph structured data are in demand. Some exist- ing algorithms require very large memory space to discover frequent subgraphs; some others discover collections of fre- quently co-occurring edges (which may be disjoint). In con- trast, we propose|in this paper|algorithms that use lim- ited memory space for discovering collections of frequently co-occurring connected edges. Evaluation results show the effectiveness of our algorithms in frequent subgraph mining from streams of linked graph structured data.