Link Prediction for Egocentrically Sampled Networks
Link Prediction for Egocentrically Sampled Networks
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DOI：
10.1080/10618600.2022.2163648
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发表时间：
2018-03
影响因子：
2.4
通讯作者：
Yun-Jhong Wu;E. Levina;Ji Zhu
中科院分区：
文献类型：
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作者：
Yun-Jhong Wu;E. Levina;Ji Zhu
摘要网络中的链路预测通常是通过对所有节点对的边的概率进行估计或排序来实现的。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。这种抽样机制需要不同的预测工具，而不是随机缺失链接的典型假设。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，估计潜在的概率矩阵估计其行空间。我们在几个合成和现实世界的网络上实证评估的方法，并表明它提供了准确的预测网络链接。补充材料包括实验代码可在线获得。
Abstract 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.