@article{breiman2001random,
  abstract = {Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to },
  author = {Breiman, Leo},
  doi = {10.1023/A:1010933404324},
  interhash = {4450d2e56555e7cb8f3817578e1dd4da},
  intrahash = {b8187107bf870043f2f93669958858f1},
  issn = {0885-6125},
  journal = {Machine Learning},
  language = {English},
  number = 1,
  pages = {5-32},
  publisher = {Kluwer Academic Publishers},
  title = {Random Forests},
  url = {http://dx.doi.org/10.1023/A%3A1010933404324},
  volume = 45,
  year = 2001
}

@book{janson2000theory,
  address = {New York; Chichester},
  author = {Janson, Svante and Luczak, Tomasz and Rucinski, Andrzej},
  interhash = {929294638db37c413b283ac468bbdade},
  intrahash = {7bb074240f72009f515123f15afecefd},
  isbn = {0471175412 9780471175414},
  publisher = {John Wiley & Sons},
  refid = {43340250},
  title = {Theory of random graphs},
  url = {http://www.amazon.com/Random-Graphs-Svante-Janson/dp/0471175412},
  year = 2000
}

@inproceedings{backstrom2011supervised,
  abstract = {Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open.</p> <p>We develop an algorithm based on <i>Supervised Random Walks</i> that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function.</p> <p>Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction.},
  acmid = {1935914},
  address = {New York, NY, USA},
  author = {Backstrom, Lars and Leskovec, Jure},
  booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining},
  doi = {10.1145/1935826.1935914},
  interhash = {94f21249839cf875da4ad8842cd37d15},
  intrahash = {999a159de862039db86fe74f808526e3},
  isbn = {978-1-4503-0493-1},
  location = {Hong Kong, China},
  numpages = {10},
  pages = {635--644},
  publisher = {ACM},
  series = {WSDM '11},
  title = {Supervised random walks: predicting and recommending links in social networks},
  url = {http://doi.acm.org/10.1145/1935826.1935914},
  year = 2011
}