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- W2621071987 abstract "The computation models we see so far are all data-intensive, where the cost of message transmission is often much higher than that of message processing, rendering the distributed execution communication-intensive. However, graph mining tasks are often computation-intensive, and cannot be efficiently executed with a data-intensive system. The vertex-centric API is also unsuitable for writing a graph mining algorithm that often checks subgraphs rather than individual vertices. This chapter introduces a couple of subgraph-centric systems for graph mining, among which only G-thinker is able to handle computation-intensive workloads. G-thinker targets at problems that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). It provides an intuitive subgraph-centric API for graph exploration, which can be used to conveniently implement various graph mining algorithms." @default.
- W2621071987 created "2017-06-09" @default.
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- W2621071987 date "2017-01-01" @default.
- W2621071987 modified "2023-09-27" @default.
- W2621071987 title "Subgraph-Centric Graph Mining" @default.
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- W2621071987 doi "https://doi.org/10.1007/978-3-319-58217-7_6" @default.
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