Abstract
Graph is a way of representing data as a web of relationships. Graphs consist of nodes and edges where nodes are concepts and edges are the relationships. Graph analytics is the science of solving problems expressed on graphs. Graph analytics is useful for finding hidden patterns, relationships, similarities and anomalies in graphs. These tasks are useful in many application areas like protein analysis, fraud detection, health care, computer security, financial data analysis, etc. Minimum description length (MDL) comes from information theory, which can be used for universal coding or universal modeling. SUBstructure Discovery Using Examples (SUBDUE) algorithm uses MDL to discover substructures. In this paper, the use of MDL for graph analytics is shown by applying MDL encoding to various graph datasets and in particular graph matching is solved using MDL. Further, comparative analysis is done to show how MDL value changes w.r.t. varying graph properties. subgen tool is used to generate graph datasets. Statistical tests are applied and we came to know in which cases MDL value changes significantly.
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Velampalli, S., Murthy Jonnalagedda, V.R. (2017). Minimum Description Length (MDL) Based Graph Analytics. In: Satapathy, S., Prasad, V., Rani, B., Udgata, S., Raju, K. (eds) Proceedings of the First International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 507. Springer, Singapore. https://doi.org/10.1007/978-981-10-2471-9_10
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DOI: https://doi.org/10.1007/978-981-10-2471-9_10
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