LSNing
LSNing is a family of methods in network analysis that emphasizes local structural similarities among nodes by examining their immediate neighborhoods in a graph. Short for Local Structural Neighborhooding, LSNing aims to quantify how alike two nodes are based on surrounding topology rather than global graph properties. In practice, LSNing refers to approaches that derive node representations or similarity scores from local neighborhoods to support predictive tasks.
The typical workflow defines a local neighborhood for each node (for example, nodes within k hops). Features
LSNing has been applied to link prediction, node classification, and anomaly detection in social, biological, and
Limitations include computational costs on large graphs, sensitivity to neighborhood size and feature choice, and interpretability