Abstract
In this paper we propose PATCOMP—a PARTICIA-based novel approach for Network motif search. The algorithm takes advantage of compression and speed of PATRICIA data structure to store the collection of subgraphs in memory and search for classification and census of network. Paper also describes the structure of PATRICIA nodes and how data structure is developed for using it for counting of subgraphs. The main benefit of this approach is significant reduction in memory space requirement particularly for larger network motifs with acceptable time performance. To assess the effectiveness of PATRICIA-based approach we compared the performance (memory and time) of this proposed approach with QuateXelero. The experiments with different networks like ecoli and yeast validate the advantage of PATRICIA-based approach in terms of reduction in memory usage by 4.4–20% for E. coli and 5.8–23.2% for yeast networks.
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Acknowledgements
Authors express their deep sense of gratitude to the Founder President of Amity University, Dr. Ashok K. Chauhan, for his keen interest in promoting research in the Amity University and has always been an inspiration for achieving greater heights.
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Himanshu, Jain, S. (2017). Impact of Memory Space Optimization Technique on Fast Network Motif Search Algorithm. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-10-3770-2_52
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DOI: https://doi.org/10.1007/978-981-10-3770-2_52
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