Research

My current research is mainly focused on information diffusion in social network. Previously I worked on developing topic-based methods for academic search and recommendation and visualzation of topic evolution, which are the theoretical part of our Academic Search Engine.

I am also interested in other machine learning related topics (eg. deep learning), data mining, espically graph mining and text mining.

Ranking papers for different topics in Citation Network.

Discovering important papers in different academic topics is known as topic-sensitive influential paper discovery. Previous works mainly find the influential papers based on the structure of citation networks but neglect the text information, while the text of documents gives a more precise description of topics. We conbined textual information to rank the papers and indentify the most important ones among them.

We have made such contributions:

• We conduct empirical observations on real-world dataset to summerize the factors that influence papers to cite.
• We propose a new, robust model to generate the citation network with topic and the importance of papers
• We achieve an improvement on the task of citation prediction and implement our model in academic recommendation.

Paper: X. Huang, C. Chen, C. Peng, X. Wu, L. Fu and X. Wang, “Topic-sensitive Influential Paper Discovery in Citation Network”, PAKDD 2018, Melbourne, Australia. pdf

Topic Evolution in Scholarly Big-data

I worked to construct a probablistic graphical model to capture the temporal evolution of topics in scientific publications.

We have made such contributions:

• We provide a topic modelling based model to capture the timestamps and contents of topics.
• We visualised the evolution map of topic with gephi
• Our model can also be applied to information retrival of academic papers.

An Interactive Topic Model for academic recommendation

I worked to construct a topic-based model to recommend scientific publications. We have made such contributions:

• We provide an interactive topic model with tree-structured priors and encode user feedback into the prior tree.
• We significantly increase the computational efficiency by adopting similar mechanism like SparseLDA.
• We propose a crowdsourcing framework for recommending publications and further modify our interactive topic model to a collaborative version. In this scenario, users with similar interests can fix a shared prior tree, which promotes article recommendation in related topics.