Many applications involve objects containing structure and rich content information, each
describing different feature aspects of the object. Graph learning and classification is a …

Traditional graph systems mainly use the iteration-based model which iteratively loads
graph blocks into memory for analysis so as to reduce random I/Os. However, this …

Graph analytical queries (GAQs) are becoming increasingly important in various domains,
including social networks, recommendation systems, and bioinformatics, among others …

Social networks are a vital mechanism to disseminate information to friends and colleagues.
In this work, we investigate an important problem—the personalized influential topic search …

As random walk is a powerful tool in many graph processing, mining and learning
applications, this paper proposes an efficient inmemory random walk engine named …

Distributed graph analysis usually partitions a large graph into multiple small-sized
subgraphs and distributes them into a cluster of machines for computing. Therefore, graph …

The problem of selecting a group of vertices under certain constraints that maximize their
joint centrality arises in many practical scenarios. In this paper, we extend the notion of …

Out-of-core random walk system has recently attracted a lot of attention as an economical
way to run billions of walkers over large graphs. However, existing out-of-core random walk …

In this paper, we study the opinion maximization problem for the leader-follower DeGroot
model of opinion dynamics in a social network modelled by a directed graph with n nodes …

We introduce and formulate two types of random-walk domination problems in graphs
motivated by a number of applications in practice (eg, item-placement problem in online …