UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning

Retweet Behavior Prediction Using Hierarchical Dirichlet Process

The task of predicting retweet behavior is an important and essential step for various social network applications, such as business intelligence, popular event prediction, and so on. Due to the increasing requirements, in recent years, the task has attracted extensive attentions. In this work, we propose a novel method using non-parametric statistical models to combine structural, textual, and...

Deceptive Answer Prediction with User Preference Graph

In Community question answering (QA) sites, malicious users may provide deceptive answers to promote their products or services. It is important to identify and filter out these deceptive answers. In this paper, we first solve this problem with the traditional supervised learning methods. Two kinds of features, including textual and contextual features, are investigated for this task. We furthe...

Personalized Retweet Prediction in Twitter

Graph Representation Learning and Graph Classification

Many real-world problems are represented by using graphs. For example, given a graph of a chemical compound, we want do determine whether it causes a gene mutation or not. As another example, given a graph of a social network, we want to predict a potential friendship that does not exist but it is likely to appear soon. Many of these questions can be answered by using machine learning methods i...

Query Representation with Global Consistency on User Click Graph

Extensive research has been conducted on query log analysis. A query log is generally represented as a bipartite graph on a query set and a URL set. Most of the traditional methods used the raw click frequency to weigh the link between a query and a URL on the click graph. In order to address the disadvantages of raw click frequency, researchers proposed the entropy-biased model, which incorpor...