Supervised Graph Inference
By Jean-Philippe Vert, Yoshihiro Yamanishi. 2004
102 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Mikhail Belkin
- Yoshua Bengio
- Jure Leskovec
- Dacheng Tao
- Bernhard Schölkopf
- Céline Brouard
- Qiang Yang
- Partha Niyogi
Citations in the corpus
- Supervised reconstruction of biological networks with local models (2007, methodology): “This algorithm, like kernel CCA, depends on a regularization parameter and a dimension of projection d, that we chose equal to 2 and 20 respectively, following the best results reported by [ 24 ].”
- Input Output Kernel Regression (2016, background): “• kML (Vert and Yamanishi, 2005): kernel Metric Learning consists in learning a new metric such that interacting proteins are close to each other, and conversely for non interacting proteins.”
- Statistical inference on graphs (2006, background): “To solve the problem, Vert and Yamanishi [24] propose an algorithm which learns a mapping of the vertices into a Euclidean space, and then put an edge between the (mapped) vertices which are closer to each other than a threshold.”
- Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples (2006, methodology): “We note that a similar algorithm has been independently proposed in Vert and Yamanishi (2005) in the context of supervised graph inference.”
- A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference (2010, methodology): “A primitive method is to average data from the multiple types of assays and reconstruct a network from the averaged data (Yamanishi et al., 2005; Vert & Yamanishi, 2005).”
- On the Convexity of Latent Social Network Inference (2010, background): “cess. Our work here is similar in a sense that we “regress” the infection times of a target node on infection times of other nodes. Additionally, our work is also related to a link prediction problem [12, 23, 18, 24] but different in a sense that this line of work assumes that part of the network is”