User-to-user interactions have become ubiquitous in Web 2.0. Users exchange emails, post on newsgroups, tag web pages, co-author papers, etc. Through these interactions, users co-produce or co-adopt content items (e.g., words in emails, tags in social bookmarking sites). We model such dynamic interactions as a user interaction network, which relates users, interactions, and content items over time. After some interactions, a user may produce content that is more similar to those produced by other users previously. We term this effect social dependency, and we seek to mine from such networks the degree to which a user may be socially dependent on another user over time. We propose a Decay Topic Model to model the evolution of a user’s preferences for content items at the topic level, as well as a Social Dependency Metric that quantifies the extent of social dependency based on interactions and content changes. Our experiments on two user interaction networks induced from real-life datasets show the effectiveness of our approach.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
SIAM International Conference on Data Mining (SDM)
City or Country
CHUA, Freddy Chong-Tat; LAUW, Hady Wirawan; and LIM, Ee Peng.
Mining Social Dependencies in Dynamic Interaction Networks. (2012). SIAM International Conference on Data Mining (SDM). Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1551