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 …

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 …

Social networks have increasingly become important and popular in modern times.
Moreover, the influence of social networks plays a vital role in various organizations …

Kemeny's constant for random walks on a graph is defined as the mean hitting time from one
node to another selected randomly according to the stationary distribution. It has found …

PageRank is a commonly used measurement in a wide range of applications, including
search engines, recommendation systems, and social networks. However, this …

For an undirected graph, its Kemeny's constant is defined as the mean hitting time of random
walks from one vertex to another chosen randomly according to the stationary distribution …

Graph propagation (GP) computation plays a crucial role in graph data analysis, supporting
various applications such as graph node similarity queries, graph node ranking, graph …

Personalized PageRank (PPR) measures the importance of vertices with respect to a source
vertex. Since real-world graphs are evolving rapidly, PPR computation methods need to be …

Given a graph G and a source node s, the single-source Personalized PageRank (SSPPR)
π (s, v) measures the importance of the node v with respect to the source node s, thus …