复杂网络链路预测的有效相似性指标
Effective and Efficient Similarity Index for Link Prediction of Complex Networks
摘要：
对不完整网络（如蛋白质-蛋白质相互作用网络）或进化网络（如网络社会中的友谊网络）中非常可能但尚未存在的链接的预测可以被视为进一步实验的指导方针或对网络用户有价值的信息。在本文中，我们引入了一个局部路径指数来估计两个节点之间的链接存在的可能性。我们提出了一个在生成链接时密度和噪声强度可控的网络模型，并收集了六个真实网络的数据。大量的数值模拟模型的网络和真实的网络证明了高的有效性和效率的本地路径索引相比，两个著名的和广泛使用的索引，共同的邻居和Katz指数。事实上，本地路径索引提供了具有竞争力的准确预测的Katz指数，而需要更少的CPU时间和内存空间，因此，这是一个强大的候选人的潜在的实际应用中的数据挖掘的巨大规模的网络。
英文摘要：
Predictions of missing links of incomplete networks like protein-protein interaction networks or very likely but not yet existent links in evolutionary networks like friendship networks in web society can be considered as a guideline for further experiments or valuable information for web users. In this paper, we introduce a local path index to estimate the likelihood of the existence of a link between two nodes. We propose a network model with controllable density and noise strength in generating links, as well as collect data of six real networks. Extensive numerical simulations on both modeled networks and real networks demonstrated the high effectiveness and efficiency of the local path index compared with two well-known and widely used indices, the common neighbors and the Katz index. Indeed, the local path index provides competitively accurate predictions as the Katz index while requires much less CPU time and memory space, which is therefore a strong candidate for potential practical applications in data mining of huge-size networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
