Toggle navigationYang Yang (杨洋)HomeResearchPublicationsAwardsI am a Ph.D. student in Department of Computer Science and Technology of Tsinghua University. I am very fortunate to be co-advised by Jie Tang and Juanzi Li. I obtained my bachelor degree in Software Engineering from Wuhan University in 2011. My research interests include social network analysis and probabilistic graphical models. More specifically, I am interested in studying information diffusion process in online social networks, and learning latent knowledge from large-scale corpus. SherlockBourne {at} gmail [dot] comSelected Research Work
How Do Your Friends on Social Media Disclose Your Emotions? (AAAI'14)
Yang Yang, Jia Jia, Shumei Zhang, Boya Wu, Qicong Chen, Juanzi Li, and Jie Tang
Mining emotions hidden in images has attracted significant interest, in particular with the rapid development of social networks. The emotional impact is very important for understanding the intrinsic meanings of images. To understand the emotional impact from images, one interesting question is: How does social effect correlate with the emotion expressed in an image? Specifically, can we leverage friends interactions (e.g., discussions) related to an image to help discover the emotions? In this paper, we formally formalize the problem and propose a novel emotion learning method by jointly modeling images posted by social users and comments added by friends. One advantage of the model is that it can distinguish those comments that are closely related to the emotion expression for an image from other irrelevant ones. Experiments on an open Flickr dataset show that the proposed model can significantly improve (+37.4% by F1) the accuracy for inferring emotions from images. More interestingly, we found that half of the improvements are due to interactions between 1% of the closest friends.
Paper:emotion.pdf | Code: EmotionLearn.zip
Forecasting Potential Diabetes Complications (AAAI'14)
Yang Yang, Walter Luyten, Lu Liu, Marie-Francine Moens, Jie Tang, and Juanzi Li
Diabetes complications often afflict diabetes patients seriously: over 68% of diabetes-related mortality is caused by diabetes complications. In this paper, we study the problem of automatically diagnosing diabetes complications from patients' lab test results. The objective problem has two main challenges: 1) feature sparseness: a patient only takes 1.26% lab tests on average, and 65.5% types of lab tests are taken by less than 10 patients; 2) knowledge skewness: it lacks comprehensive detailed domain knowledge of association between diabetes complications and lab tests.  To address these challenges, we propose a novel probabilistic model called Sparse Factor Graph Model (SparseFGM). SparseFGM projects sparse features onto a lower-dimensional latent space, which alleviates the problem of sparseness. SparseFGM is also able to capture the associations between complications and lab tests, which help handle the knowledge skewness.We evaluate the p