2018/03/12 by Tianxi Li, Yun-Jhong Wu, Wu, Yun-Jhong +4 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Opinion Dynamics and Social Influence #cs.LG #stat.CO #stat.ML
paper · pdf · doi:10.48550/arxiv.1803.04084
arxiv created 2018/03/12 · openalex publication_date 2018/03/12 · arxiv updated 2018/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.