With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques.

@incollection{liang2011finding, author = {Liang, Yicong and Li, Qing and Qian, Tieyun}, title = {Finding Relevant Papers Based on Citation Relations}, editor = {Wang, Haixun and Li, Shijun and Oyama, Satoshi and Hu, Xiaohua and Qian, Tieyun}, booktitle = {Web-Age Information Management}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2011}, volume = {6897}, pages = {403--414}, url = {http://dx.doi.org/10.1007/978-3-642-23535-1_35}, doi = {10.1007/978-3-642-23535-1_35}, isbn = {978-3-642-23534-4}, keywords = {item, recommender, citation}, abstract = {With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques.} }

%0 = incollection %A = Liang, Yicong and Li, Qing and Qian, Tieyun %B = Web-Age Information Management %C = Berlin/Heidelberg %D = 2011 %I = Springer %T = Finding Relevant Papers Based on Citation Relations %U = http://dx.doi.org/10.1007/978-3-642-23535-1_35

@article{li2006cyberbullying, author = {Li, Qing}, title = {Cyberbullying in Schools: A research of gender differences}, journal = {School Psychology International}, year = {2006}, number = {2}, pages = {157-170}, keywords = {Cybermobbing_Gender} }

%0 = article %A = Li, Qing %D = 2006 %T = Cyberbullying in Schools: A research of gender differences

@article{li2005bottle, author = {Li, Qing}, title = {New bottle but old wine: A research of cyberbullying in schools}, journal = {Computers in human behavior}, year = {2005}, number = {4}, pages = {1177-1791}, keywords = {Cybermobbing} }

%0 = article %A = Li, Qing %D = 2005 %T = New bottle but old wine: A research of cyberbullying in schools