Abstract
The task of detecting pattern or sub-graph in a large graph has applications in large areas such as biology, computer vision, computer-aided design, electronics, intelligence analysis, and social networks. So work on graph-based pattern detection has a wide range of research fields. Since the characteristics and application requirements of graph vary, graph-based detection is not the only problem, but it is a set of graph-related problems. This paper proposes a new approach for detection of sub-graph or pattern from a weighted graph with edge weight detection method using graph mining techniques. The edge detection method is proposed since most of the graphs are weighted one. Hence this paper proposes an algorithm named EdWePat for detection of patterns or sub-graphs with edge weight detection rather node value.
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Rao, B., Mishra, S. (2019). An Approach to Detect Patterns (Sub-graphs) with Edge Weight in Graph Using Graph Mining Techniques. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_71
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DOI: https://doi.org/10.1007/978-981-10-8055-5_71
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