Expert Systems With Applications

Graph-Based Gene Embedding.

GraphC: Hierarchical clustering of signed graph networks

• graphC is a k-independent hierarchical clustering method for signed graphs. • It scales to large sparse signed networks with tens of millions of edges. • It avoids spectral decomposition and eigenvalue pollution issues without requiring training or hyperparameter tuning. • It treats positive and negative edges symmet...

Interpretable GNN with Stable Concepts as Neurons

Differentially Private Graph Learning: A Survey

This survey presents the first comprehensive and systematic review of Differentially Private Graph Learning (DPGL), and organizes existing DPGL methods into four categories based on the granularity of privacy protection, namely node-level, edge-level, graph-level, and node-level.

Networks in Social Science

This workshop covers the foundations of social network analysis in RStudio, from data preparation and centrality measures to visualisation and interpretation. It also introduces a practical research workflow and an initial overview of Exponential Random Graph Models for advanced social science applications.

Learning for Heterogeneous Information Networks

This paper introduces a novel prototype-enhanced hypergraph learning approach for node classification in HINs using hypergraphs instead of graphs, which captures higher-order relationships among nodes and extracts semantic information without relying on metapaths.