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 …

Abstract Knowledge graphs serve as a pivotal framework for the structured representation of
information regarding entities and relations. However, in the real world, these knowledge …

Graph Neural Networks (GNNs) have achieved remarkable performance in various
applications, including social media analysis, computer vision, and natural language …

The forest matrix of a graph, particularly its diagonal elements, has far-reaching implications
in network science and machine learning. The state-of-the-art algorithms for the diagonal of …

Effective resistance (ER) is a fundamental metric for measuring node similarities in a graph,
and it finds applications in various domains including graph clustering, recommendation …

In bipartite graph analysis, similarity measures play a pivotal role in various applications.
Among existing metrics, the Bidirectional Hidden Personalized PageRank (BHPP) stands …

Network embedding has numerous practical applications and has received extensive
attention in graph learning, which aims at map** vertices into a low-dimensional and …

Personalized PageRank (PPR) is an extensively studied and applied node proximity
measure in graphs. For a pair of nodes $ s $ and $ t $ on a graph $ G=(V, E) $, the PPR …

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 …

The pseudo-inverse of a graph Laplacian matrix, denoted as $ L^\dagger $, finds extensive
application in various graph analysis tasks. Notable examples include the calculation of …