MENG , Zide

We propose an approach to detect topics, overlapping communities of interest, expertise, trends andactivities in user-generated content sites and in particular in question-answering forums such asStackOverFlow. We first describe QASM (Question & Answer Social Media), a system based on socialnetwork analysis to manage the two main resources in...

Users in question-answer sites generate huge amounts of high quality and highly reusable information. This information can be categorized by topics but since users' interests change with time, it's important to uncover the temporal patterns and trends in their activity to detect their current expertize. These temporal variations remained...

In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and in particular interest groups. Identifying these users' communities and the interests that bind them can help us assist their life-cycle. Certain kinds of online communities such as...

In many social networks, people interact based on their relationship network. Community detection algorithms are then useful to reveal the sub-structures of a network. Identifying these users' communities can help us assist their life-cycle. However, in certain kinds of online communities such as question-and-answer (Q&A) sites or forums,...

In this paper, we describe the QASM (Question & Answer Social Media) system based on social network analysis to manage the two main resources in CQA sites: users and contents. We first present the QASM vocabulary used to formalize both the level of interest and the expertise of users on topics. Then we present our method to extract this...

In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and in particular interest groups. Identifying these users' communities and the interests that bind them can help us assist their life-cycle. Certain kinds of online communities such as...

In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and help us find interest groups. Identifying these social communities can bring benefit to understanding and predicting users behaviors. However, for some kind of online community sites...

More and more Internet companies rely on large scale data analysis as part of their core services for tasks such as log analysis, feature extraction or data filtering. Map-Reduce, through its Hadoop implementation, has proved to be an efficient model for dealing with such data. One important challenge when performing such analysis is to predict...

More and more Internet companies rely on large scale data analysis as part of their core services for tasks such as log analysis, feature extraction or data filtering. Map-Reduce, through its Hadoop implementation, has proved to be an efficient model for dealing with such data. One important challenge when performing such analysis is to predict...