Graph representation learning (GRL) is a powerful tool for graph analysis, which has gained
massive attention from both academia and industry due to its superior performance in …

Bipartite graph embedding (BGE), as the fundamental task in bipartite network analysis, is to
map each node to compact low-dimensional vectors that preserve intrinsic properties. The …

A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely
used to model real-world data, such as user-item purchase records, author-article …

Graph neural networks have been widely used to learn node representations for many real-
world static graphs. In general, they learn node representations by recursively aggregating …

Graphs are used as a model of complex relationships among data in biological science
since the advent of systems biology in the early 2000. In particular, graph data analysis and …

As the field of data science continues to evolve, bipartite graphs have emerged as a
fundamental structure in numerous applications, drawing significant interest from both …

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

Graph representation learning (GRL) is to encode graph elements into informative vector
representations, which can be used in downstream tasks for analyzing graph-structured data …

Bipartite networks are common in real-world applications, where link prediction helps
understand network evolution and make recommendations. Traditional methods have two …

The Multi-Criteria Test Suite Minimization (MCTSM) problem aims to refine test suites by
removing redundant test cases, guided by adequacy criteria such as code coverage or fault …