用于链接预测的弹簧电模型
Spring-Electrical Models For Link Prediction
摘要：
我们提出了一个链接预测算法，是基于弹簧电模型。研究这些模型的想法来自于弹簧电模型已成功用于网络可视化的事实。良好的网络可视化通常意味着在网络拓扑方面相似的节点，例如，连接的和/或属于一个集群，倾向于被可视化为彼此靠近。因此，我们假设所获得的网络布局中的节点之间的欧几里德距离与它们之间的链接的概率相关。我们对几个流行的基线评估所提出的方法，并证明其灵活性，将其应用于无向，有向和二分网络。
英文摘要：
We propose a link prediction algorithm that is based on spring-electrical models. The idea to study these models came from the fact that spring-electrical models have been successfully used for networks visualization. A good network visualization usually implies that nodes similar in terms of network topology, e.g., connected and/or belonging to one cluster, tend to be visualized close to each other. Therefore, we assumed that the Euclidean distance between nodes in the obtained network layout correlates with a probability of a link between them. We evaluate the proposed method against several popular baselines and demonstrate its flexibility by applying it to undirected, directed and bipartite 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。
