Link Prediction for Egocentrically Sampled Networks
针对自我中心抽样得到的网络数据，提出一种新的高效链接预测算法，通过估计概率矩阵的行空间来预测缺失链接，在合成和真实网络上验证了准确性。
Link prediction in networks is typically accomplished by estimating or ranking the probabilities of edges for all pairs of nodes. In practice, especially for social networks, the data are often collected by egocentric sampling, which means selecting a subset of nodes and recording all of their edges. This sampling mechanism requires different prediction tools than the typical assumption of links missing at random. We propose a new computationally efficient link prediction algorithm for egocentrically sampled networks, estimating the underlying probability matrix by estimating its row space. We empirically evaluate the method on several synthetic and real-world networks and show that it provides accurate predictions for network links. Supplemental materials including the code for experiments are available online.