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 are drawn from the neighborhood, including motif counts, local clustering, degree statistics, and short-path measures, optionally augmented with node attributes. These features are aggregated into an LSN vector, which can be used to compute similarities, or to train a model or embedding that encodes local structure.

LSNing has been applied to link prediction, node classification, and anomaly detection in social, biological, and knowledge-graph networks. It is often used alongside other representation methods such as graph neural networks or graph kernels to provide complementary local signals.

Limitations include computational costs on large graphs, sensitivity to neighborhood size and feature choice, and interpretability challenges. As there is no single standard definition, practitioners may implement different variants under the LSNing label, which can complicate cross-study comparisons.