FORank: Fast ObjectRank for Large Heterogeneous Graphs
FORank: Fast ObjectRank for Large Heterogeneous Graphs
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DOI：
10.1145/3184558.3186950
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发表时间：
2018-04
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影响因子：
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通讯作者：
Tomoki Sato;Hiroaki Shiokawa;Yuto Yamaguchi;H. Kitagawa
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文献类型：
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作者：
Tomoki Sato;Hiroaki Shiokawa;Yuto Yamaguchi;H. Kitagawa
ObjectRank是一种流行的图挖掘方法，它使我们能够评估异构图上每个顶点的重要性。然而，将其应用于大型图的计算代价很高，因为ObjectRank需要迭代地计算所有顶点的重要性。在这项工作中，我们提出了一个快速的对象排名算法，FORank，准确地逼近关键字搜索结果。FORank迭代地修剪其收敛分数在迭代计算过程中对结果影响较小的顶点。实验表明，FORank的计算速度是ObjectRank的7倍，准确率接近90%以上。
ObjectRank is one of the popular graph mining methods that enables us to evaluate the importance of each vertex on heterogeneous graphs. However, it is computationally expensive to apply it to large graphs since ObjectRank needs to compute the importance of all vertices iteratively. In this work, we present a fast ObjectRank algorithm,FORank, that accurately approximates the keyword search results. FORank iteratively prunes vertices whose convergence score likely has less impact on the results during iterative computation. The experiments showed that FORank runs 7 times faster than ObjectRank computation with over 90% accuracy approximation.