Details

Autor(en) / Beteiligte

• Li, Tianxi
• Wu, Yun-Jhong
• Levina, Elizaveta
• Zhu, Ji

Titel

Link Prediction for Egocentrically Sampled Networks

Ist Teil von

• Journal of computational and graphical statistics, 2023-10, Vol.32 (4), p.1296-1319

Ort / Verlag

Alexandria: Taylor & Francis

Erscheinungsjahr

2023

Quelle

Taylor & Francis

Beschreibungen/Notizen

• 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.