We develop an algorithm based on *Supervised Random Walks* 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.

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 }