BMI | talk5
BMI | talk5
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Wenqing Lin

Senior Researcher, NTU

April 21, 2017


Graph data have been so prevalent that efficiently obtaining useful information from them is highly demanded. Given massive amounts of graph data for analytics, people are often interested in their subgraphs by the processes of mining and querying. However, due to the enormous number of subgraphs in the massive graph data, these processes are extremely expensive. In this talk, we first introduce efficient algorithms for subgraph mining with parallel computing techniques. Then, we discuss the applications, such as node similarity computation and graph query processing, that utilize subgraphs to achieve better performances. Finally, the talk is concluded with the future works on large-scale graph analytics.


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