Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: How can  predict Smith's main hobby if  know the main hobby of Smith's friends? Can  measure the confidence in our predic- tion if  are given the main hobby of only a few of Smith's friends? ,  focus on how to estimate the confidence on the node classi- fication problem. Providing a confidence level for the classification prob- lem is important because most nodes in real world networks tend to have few neighbors, and thus, a small amount of evidence. Our contributions are three-fold: (a) novel algorithm;  propose a semi-supervised learning algorithm that converges fast, and provides the confidence estimate (b) theoretical analysis;  show the solid theoretical foundation of our algo- rithm and the connections to label propagation and Bayesian inference (c) empirical analysis;  perform extensive experiments on three dif- ferent real networks. Specifically, the experimental results demonstrate that our algorithm outperforms other algorithms on graphs with less smoothness and low label density.
['Yuto Yamaguchi', 'Christos Faloutsos', 'Hiroyuki Kitagawa']
I am writing a research paper on a idea SocNL: Bayesian Label Propagation with Confidence
[{"from": "human", "value": "I am writing a research paper on a idea SocNL: Bayesian Label Propagation with Confidence"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: How can  predict Smith's main hobby if  know the main hobby of Smith's friends? Can  measure the confidence in our predic- tion if  are given the main hobby of only a few of Smith's friends? ,  focus on how to estimate the confidence on the node classi- fication problem. Providing a confidence level for the classification prob- lem is important because most nodes in real world networks tend to have few neighbors, and thus, a small amount of evidence. Our contributions are three-fold: (a) novel algorithm;  propose a semi-supervised learning algorithm that converges fast, and provides the confidence estimate (b) theoretical analysis;  show the solid theoretical foundation of our algo- rithm and the connections to label propagation and Bayesian inference (c) empirical analysis;  perform extensive experiments on three dif- ferent real networks. Specifically, the experimental results demonstrate that our algorithm outperforms other algorithms on graphs with less smoothness and low label density."}]