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Efficient Subgraph Indexing for Biochemical Graphs

: The dynamic nature of graph-structured data demands fast subgraph query processing to solve real-world problems such as identifying spammers in social networks, fraud detection in the financial system, and finding motifs in biological networks. The need for an efficient subgraph search has motivated the study for filtering the candidate graphs using the filter-then-verify framework with minimal indexing size. This paper presents an efficient in-memory index structure for indexing the paths in the transaction graph database. Our radix tree-based index structure addresses the issue of high memory consumption related to trie for representing biochemical datasets. Furthermore, we also contrast various containers used in the radix nodes. We demon-strate empirically the benefits of compressing the common prefixes in the path by achieving 20% reduction in the indexing size than a trie-based implementation.