Machine Learning Methods with Link-Level Features

This paper describes graph link level features. We describe three different kinds of them. We discuss distance-based characteristics that consumers might use, such as the shortest path between two nodes, which does not account for neighborhood overlaps. Then we discuss neighborhood overlap metrics like Common neighbors, Jaccard coefficient, and the Adamic-Adar index that measures how many neighbors a pair of nodes share in a fine-grained manner. The issue with this is that the metric will return a value of 0 for nodes that are more than two hops away or that do not have any neighbors. The global neighborhood overlap metrics, for instance, like Katz use the global graph structure to give us a score for a pair of nodes, and the Katz index counts the number of pets of all lands between a pair of nodes where these paths are discounted exponentially with their length.