Partially Observed Graph
A Partially Observed Graph is a graph that is a partially observed object.
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- Counter-Example(s):
- See: Temporal Link Prediction.
References
2011
- (Menon & Elkan, 2011) ⇒ Aditya Krishna Menon, and Charles Elkan. (2011). “Link Prediction via Matrix Factorization.” In: Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II. ISBN:978-3-642-23782-9
- QUOTE: There are two types of link prediction: (i) structural, where the input is a partially observed graph, and we wish to predict the status of edges for unobserved pairs of nodes, and (ii) temporal, where we have a sequence of fully observed graphs at various time steps as input, and our goal is to predict the graph state at the next time step. Both problems have important practical applications, such as predicting interactions between pairs of proteins and [[recommending friends in social networks]] respectively.