CSE Speaker Series – Dr. Huiping Cao

Dr. Huiping Cao from NMSU will be here on Friday, Sepetmber 23 from 11:00-12:00 in Cramer 221 to discuss “Aspect-level Influence Discovery from Graphs”.

 

Abstract:

Graphs have been widely used to represent objects and object connections in applications such as the Web, social networks, and citation networks. Mining influence relationships from graphs has gained increasing interests in recent years because providing information on how graph objects influence each other can facilitate graph exploration, graph search, and connection recommendations. In this talk, Dr. Cao will present the problem of detecting influence aspects, on which objects are connected, and influence degree (or influence strength), with which one graph node influences another graph node on a given aspect. Dr. Cao will present a systematic approach to extract influence aspects and learn aspect-level influence strength. In particular, she will first present a novel instance-merging based method to extract influence aspects from the context of object connections. She then introduces two generative models, Observed Aspect Influence Model (OAIM) and Latent Aspect Influence Model (LAIM), to model the topological structure of graphs, the text content associated with graph objects, and the context in which the objects are connected. Dr. Cao’s team has conducted extensive experiments on synthetic and real data sets. The experimental results show that the proposed models can discover more effective results than existing approaches.