The ongoing rapid expansion of the Internet greatly increases the necessity of effective
recommender systems for filtering the abundant information. Extensive research for …

Graph clustering, also known as community detection, is a long-standing problem in data
mining. However, with the proliferation of rich attribute information available for objects in …

Social friendship has been shown beneficial for item recommendation for years. However,
existing approaches mostly incorporate social friendship into recommender systems by …

The popularity of location-based social networks available on mobile devices means that
large, rich datasets that contain a mixture of behavioral (users visiting venues), social (links …

Online social networks (OSNs) recommend new friends to registered users based on local-
based features of the graph (ie based on the number of common friends that two users …

This paper presents a survey as well as an empirical comparison and evaluation of seven
kernels on graphs and two related similarity matrices, that we globally refer to as “kernels on …

To reveal information hiding in link space of bibliographical networks, link analysis has been
studied from different perspectives in recent years. In this paper, we address a novel …

Conventional research on similarity search focuses on measuring the similarity between
objects with the same type. However, in many real-world applications, we need to measure …

Graphs are fundamental data structures and have been employed for centuries to model
real-world systems and phenomena. Random walk with restart (RWR) provides a good …

Social networks enable users to create different types of personal items. In dealing with
serious information overload, the major problems of social recommendation are sparsity and …