复杂网络中链接预测的局部度阻塞模型
Local degree blocking model for link prediction in complex networks
摘要：
通过准确识别网络中缺失和不可靠的链路来恢复和重建网络是网络分析和挖掘领域的一项重要任务。本文通过研究一种特殊的局部结构，即由一个节点和它的所有近邻节点组成的度块，发现它包含了复杂网络链路形成的重要统计特征。因此，我们提出了一个无参数的局部阻塞（LB）预测，定量检测在给定的网络中，通过局部链接密度计算的链接形成。在六个真实网络上进行的实验结果表明，新索引在大多数测试网络上的性能优于其他传统的基于局部相似性的方法。进一步分析了LB指标与其他两种方法的相关性，发现LB指标具有与PA指标和基于最短路径的指标相似的特征，实证验证了LB指标同时捕捉到的大度原则和最短路径原则共同驱动复杂网络中的链接形成。
英文摘要：
Recovering and reconstructing networks by accurately identifying missing and unreliable links is a vital task in the domain of network analysis and mining. In this article, by studying a specific local structure, namely a degree block having a node and its all immediate neighbors, we find it contains important statistical features of link formation for complex networks. We therefore propose a parameter-free local blocking (LB) predictor to quantitatively detect link formation in given networks via local link density calculations. The promising experimental results performed on six real-world networks suggest that the new index can outperform other traditional local similarity-based methods on most of tested networks. After further analyzing the scores' correlations between LB and two other methods, we find that the features of LB index are analogous to those of both PA index and short-path-based index, which empirically verify that large degree principle and short path principle simultaneously captured by the LB index are jointly driving link formation in complex networks.
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