Abstract
This paper proposes an efficient method for indexing and mining graph database. Most existing approaches are based on frequent sub-structures such as edges, paths, or subgraphs. However, as the size of graphs increases, such index structure grows drastically in size for avoiding performance degradation. This yields a requirement for constructing a more compact index structure and introducing more informative indexing items into this index to increase its pruning power. In this paper, we demonstrate that degree information can help solve this problem. Based on this idea, we propose a new index structure (D-index) which uses the subgraph and its degree information as the indexing item. Our empirical study shows that D-index achieves remarkable improvement in performance over the state-of-the-art approach.
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Shang, H., Jin, X. (2006). Indexing and Mining of Graph Database Based on Interconnected Subgraph. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_118
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DOI: https://doi.org/10.1007/11875581_118
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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