Abstract
Data mining and knowledge inferring over knowledge graph has gained particular attention and been widely applied in industry over the past years. These works can be generalized to graph computing and graph analysis tasks, such as shortest path search, hop-constrained reachability, PageRank, triangle counting, and closeness centrality computation. However, existing graph database query language (SPARQL, Gremlin, etc.) dose not implement these algorithms. Therefore, CCKS 2022 holds a benchmark of graph computing algorithm based on graph database. We implemented all these five algorithms mentioned above based on graph database and made many optimizations to improve computation efficiency. On the final leaderboard, we took the first place, which shows extremely high practicality and efficiency.
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Wei, J., Wu, S., Jia, J., Liu, Z. (2022). Implementation and Optimization of Graph Computing Algorithms Based on Graph Database. In: Zhang, N., Wang, M., Wu, T., Hu, W., Deng, S. (eds) CCKS 2022 - Evaluation Track. CCKS 2022. Communications in Computer and Information Science, vol 1711. Springer, Singapore. https://doi.org/10.1007/978-981-19-8300-9_13
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DOI: https://doi.org/10.1007/978-981-19-8300-9_13
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