Parallel Link Prediction in Complex Network Using MapReduce
Parallel Link Prediction in Complex Network Using MapReduce
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发表时间：
2012
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通讯作者：
Rao Jun
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作者：
Rao Jun
为了将链路预测方法应用到大规模复杂网络中，本文设计并实现了一种基于MapReduce的并行链路预测算法，该算法通过局部信息包含9个相似度指数。并行链路预测算法在稀疏网络中的时间复杂度为O(N)。首先，在公共数据集上验证了算法的有效性，随着提取因子的增加，查全率上升，精度下降。在10个多种网络类型的大规模数据集上的实验结果表明，并行链路预测算法更加有效。提出了AUC的上下界（接收者工作特征曲线下的面积）。实验结果表明，上下界的中位数更接近AUC的真实值，重点关注的是预测分数是否为零而不是实际分数值。在大多数拓扑特征中，网络平均聚类系数对AUC的影响最大，随着网络平均聚类系数的增加，AUC增加。
To apply link prediction methods into large-scale complex network,this paper designs and implements a parallel link prediction algorithm based on MapReduce,which includes nine similarity Indices via local information.The parallel link prediction algorithm has a time complexity of O(N) in sparse networks.First,the paper verifies the validity of the algorithm on public datasets,increase in the extraction factor,recall ascends,and precision descends.The experimental results on ten large-scale datasets of variety network types show that the parallel link prediction algorithm is more effective than traditional ones,and its running time decreases with more compute units.The upper and lower bounds of AUC(area under a receiver operating characteristic curve) are proposed.The experimental results show the median of the upper and lower bounds are close to the real value of AUC,which focuses on whether prediction score is zero rather than the actual score value.The network average clustering coefficient has the greatest impact on AUC among most topological features and AUC rises as the network average clustering coefficient increases.