Scalable mining of large disk-based graph databases

Mining frequent structural patterns from graph databases is an interesting problem with broad applications. Most of the previous studies focus on pruning unfruitful search subspaces effectively, but few of them address the mining on large, disk-based databases. As many graph databases in applications cannot be held into main memory, scalable mining of large, disk-based graph databases remains a challenging problem. In this paper, we develop an effective index structure, ADI (for adjacency index), to support mining various graph patterns over large databases that cannot be held into main memory. The index is simple and efficient to build. Moreover, the new index structure can be easily adopted in various existing graph pattern mining algorithms. As an example, we adapt the well-known gSpan algorithm by using the ADI structure. The experimental results show that the new index structure enables the scalable graph pattern mining over large databases. In one set of the experiments, the new...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Chen Wang, Wei Wang 0009, Jian Pei, Yongtai Zhu, Baile Shi
• Keywords: Data Mining, Disk-based Graph Databases, Graph Pattern Mining, KDD 2004, Mining Various Graph