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.
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Notes
- 1.
http:///www.cis.uab.edu/yanda/gthinker.
- 2.
http:///www.cis.uab.edu/yanda/gthinker.
- 3.
You may simply use the hash-partitioner that distributes vertices to workers by hashing vertex ID. Please refer to an example application code for its usage.
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Yan, D., Tian, Y., Cheng, J. (2017). Subgraph-Centric Graph Mining. In: Systems for Big Graph Analytics. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-58217-7_6
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