The core message of this article is to present MANTRA, a rigorous framework for efficiently approximating the temporal betweenness centrality of nodes and temporal edges in large temporal graphs.
A new role similarity metric, ForestSim, is proposed that can efficiently process top-k similarity search on large networks by leveraging spanning rooted forests of graphs.
The author proposes a method, NORA, to efficiently evaluate node influence removal in graphs using gradient approximation, reducing time and complexity.
In analyzing graph datasets for node classification, the authors explore homophily measures and propose a new characteristic called label informativeness to distinguish heterophilous graphs. Adjusted homophily is recommended as a reliable measure of homophily.