Link prediction for egocentrically sampled networks
arXiv:1803.04084
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, 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.
References in corpus (9)
- Link Prediction in Complex Networks: A Survey
- The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices
- Hyperbolic Geometry of Complex Networks
- Matrix estimation by Universal Singular Value Thresholding
- Modeling homophily and stochastic equivalence in symmetric relational data
- Topological implications of negative curvature for biological and social networks
- Governance in Social Media: A case study of the Wikipedia promotion process
- A nonparametric two-sample hypothesis testing problem for random dot product graphs
- Estimating network edge probabilities by neighborhood smoothing