Lavrank

Lavrank is a term used in data analysis and information retrieval to denote a family of graph-based ranking methods that integrate local similarity information with global authority signals to produce item rankings. In this approach, data items are represented as nodes in a graph, with edges encoding relationships such as similarity, interaction, or co-occurrence. The method emphasizes local neighborhood structure while maintaining influence from globally important items, aiming to produce robust and scalable rankings.

The algorithmic idea behind lavrank involves constructing a sparse graph G = (V, E) and assigning an initial score to each node, often informed by prior knowledge or feature-based relevance. Scores are updated iteratively by blending contributions from neighboring nodes with the node’s prior. A typical update uses a damping factor to balance local and global influence, for example blending the average of neighbors’ scores with a node’s prior score and then normalizing. Repeated updates continue until convergence. This local-averaged update makes the computation amenable to large datasets when implemented with sparse matrix operations.

Applications and evaluation: Lavrank is used in search ranking, recommender systems, and document prioritization where both local similarity and global prominence matter. It can improve ranking stability in cases with uneven connectivity and is relatively robust to noisy edges when the local neighborhood is informative. Performance depends on the graph construction, edge weighting, and the chosen damping parameter, and is often complemented by feature-based priors.

Variants and related terms: Several variants exist that emphasize local structure or global priors (for example lavrank-L or lavrank-G). Related approaches include PageRank, HITS, personalized PageRank, and graph-based semi-supervised ranking. The term is not universally standardized, and implementations may differ in details and notation.