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Cane Leung

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3 papers

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AAAI Conference 2016 Conference Paper

Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning

Ying Wei
Yin Zhu
Cane Leung
Yangqiu Song
Qiang Yang

Ubiquitous computing tasks, such as human activity recognition (HAR), are enabling a wide spectrum of applications, ranging from healthcare to environment monitoring. The success of a ubiquitous computing task relies on sufﬁcient physical sensor data with groundtruth labels, which are always scarce due to the expensive annotating process. Meanwhile, social media platforms provide a lot of social or semantic context information. People share what they are doing and where they are frequently in the messages they post. This rich set of socially shared activities motivates us to transfer knowledge from social media to address the sparsity issue of labelled physical sensor data. In order to transfer the knowledge of social and semantic context, we propose a Co-Regularized Heterogeneous Transfer Learning (CoHTL) model, which builds a common semantic space derived from two heterogeneous domains. Our proposed method outperforms state-of-the-art methods on two ubiquitous computing tasks, namely human activity recognition and region function discovery.

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AAAI Conference 2015 Conference Paper

RAIN: Social Role-Aware Information Diffusion

Yang Yang
Jie Tang
Cane Leung
Yizhou Sun
Qicong Chen
Juanzi Li
Qiang Yang

Information diffusion, which studies how information is propagated in social networks, has attracted considerable research effort recently. However, most existing approaches do not distinguish social roles that nodes may play in the diffusion process. In this paper, we study the interplay between users’ social roles and their influence on information diffusion. We propose a Role-Aware INformation diffusion model (RAIN) that integrates social role recognition and diffusion modeling into a unified framework. We develop a Gibbssampling based algorithm to learn the proposed model using historical diffusion data. The proposed model can be applied to different scenarios. For instance, at the micro-level, the proposed model can be used to predict whether an individual user will repost a specific message; while at the macro-level, we can use the model to predict the scale and the duration of a diffusion process. We evaluate the proposed model on a real social media data set. Our model performs much better in both micro- and macro-level prediction than several alternative methods.

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AAAI Conference 2014 Conference Paper

Role-Aware Conformity Modeling and Analysis in Social Networks

Jing Zhang
Jie Tang
Honglei Zhuang
Cane Leung
Juanzi Li

Conformity influence is the inclination of a person to be influenced by others. In this paper, we study how the conformity tendency of a person changes with her role, as defined by her structural properties in a social network. We first formalize conformity influence using a utility function based on the conformity theory from social psychology, and then propose a probabilistic graphical model, referred to as Role-Conformity Model (RCM), for modeling the role-aware conformity influence between users by incorporating the utility function. We apply the proposed RCM to several academic research networks, and discover that people with higher degree and lower clustering coefficient are more likely to conform to others. We also evaluate RCM through the task of word usage prediction in academic publications, and show significant improvements over baseline models.

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