The ranking of network node importance is one of the most essential problems in the field of
network science. Node ranking algorithms serve as an essential part in many application …

Temporal graph neural networks (T-GNNs) are state-of-the-art methods for learning
representations over dynamic graphs. Despite the superior performance, T-GNNs still suffer …

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs.
For a pair of nodes and, the PPR value equals the probability that an-discounted random …

Many applications require to learn, mine, analyze and visualize large-scale graphs. These
graphs are often too large to be addressed efficiently using conventional graph processing …

Given an input graph G and a node v in G, homogeneous network embedding (HNE) maps
the graph structure in the vicinity of v to a compact, fixed-dimensional feature vector. This …

\em Personalized PageRank (PPR) stands as a fundamental proximity measure in graph
mining. Given an input graph G with the probability of decay α, a source node s and a target …

Fraud detection is a pressing challenge for most financial and commercial platforms. In this
paper, we study the processing pipeline of fraud detection in a large e-commerce platform of …

We study the problem of Quality-of-Service (QoS)-Aware Personalized PageRank (PPR)
computation. Existing studies mostly focus on improving the PPR query processing time …

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 …

Graph label propagation (LP) is a core component in many downstream applications such
as fraud detection, recommendation and image segmentation. In this paper, we propose …