Common-neighbour methods estimate link likelihood from the existing topology of a graph.

A common-neighbour score counts, or otherwise weights, the vertices adjacent to both candidate endpoints. The intuition is local homophily: nodes embedded in partly shared social or functional contexts have greater structural affinity. CCPA extends this family by combining common-neighbour information with centrality, while evaluation across eight standard data sets reported improved predictability relative to established algorithms. Such scores remain correlational; they do not identify the social mechanism that creates an edge.