Link direction for link prediction
Link direction for link prediction
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DOI：
10.1016/j.physa.2016.11.129
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发表时间：
2017-03
影响因子：
3.3
通讯作者：
K. Shang;M. Small;Weisheng Yan
中科院分区：
文献类型：
--
作者：
K. Shang;M. Small;Weisheng Yan
几乎所有关于链接预测的研究都集中在使用网络的属性来预测节点对之间的链接的存在。不幸的是，以前的方法很少考虑链接预测的链接方向的作用。事实上，许多现实世界的复杂网络都是有向的，忽略链路方向将意味着忽略重要信息。在这项研究中，我们提出了一个相位动态算法的有向网络节点的链接方向的作用进行分析，并证明了双向链接和单向链接有不同的作用，在链接预测和网络结构的形成。由此，我们提出了新的方向预测方法，并使用六个真实的网络来测试我们的算法。在真实的网络中，我们发现，与一对由单向链路连接的节点相比，一对由双向链路连接的节点总是有更高的概率连接到只有双向链路的公共邻居（或相反，由单向链路）。我们认为，在真实的网络中，双向链接通常会为链接预测和网络结构形成提供更多信息。此外，我们提出了一个新的方向随机化算法，以证明链接的方向在链接预测和网络结构的形成中起着重要的作用。
Almost all previous studies on link prediction have focused on using the properties of the network to predict the existence of links between pairs of nodes. Unfortunately, previous methods rarely consider the role of link direction for link prediction. In fact, many real-world complex networks are directed and ignoring the link direction will mean overlooking important information. In this study, we propose a phase-dynamic algorithm of the directed network nodes to analyse the role of link directions and demonstrate that the bi-directional links and the one-directional links have different roles in link prediction and network structure formation. From this, we propose new directional prediction methods and use six real networks to test our algorithms. In real networks, we find that compared to a pair of nodes which are connected by a one-directional link, a pair of nodes which are connected by a bi-directional link always have higher probabilities to connect to the common neighbours with only bi-directional links (or conversely by one-directional links). We suggest that, in the real networks, the bi-directional links will generally be more informative for link prediction and network structure formation. In addition, we propose a new directional randomized algorithm to demonstrate that the direction of the links plays a significant role in link prediction and network structure formation.