自我中心抽样网络的链路预测
Link prediction for egocentrically sampled networks
摘要：
网络中的链路预测通常通过估计或排序所有节点对的边的概率来实现。在实践中，特别是对于社交网络，数据通常通过以自我为中心的采样来收集，这意味着选择节点的子集并记录它们的所有边。这种采样机制需要与链路随机缺失的典型假设不同的预测工具。我们提出了一个新的计算效率的链接预测算法的自我中心采样网络，估计其行空间的潜在概率矩阵。对于通过采样行创建的网络，我们的方法优于许多流行的链接预测和图子估计技术。
英文摘要：
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, which estimates the underlying probability matrix by estimating its row space. For networks created by sampling rows, our method outperforms many popular link prediction and graphon estimation techniques.
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。
