Github user vasia commented on a diff in the pull request: https://github.com/apache/flink/pull/1980#discussion_r63891097 --- Diff: docs/apis/batch/libs/gelly.md --- @@ -2051,6 +2052,26 @@ The algorithm takes a directed, vertex (and possibly edge) attributed graph as i vertex represents a group of vertices and each edge represents a group of edges from the input graph. Furthermore, each vertex and edge in the output graph stores the common group value and the number of represented elements. +### Jaccard Index + +#### Overview +The Jaccard Index measures the similarity between vertex neighborhoods. Scores range from 0.0 (no common neighbors) to +1.0 (all neighbors are common). + +#### Details +Counting common neighbors for pairs of vertices is equivalent to counting the two-paths consisting of two edges +connecting the two vertices to the common neighbor. The number of distinct neighbors for pairs of vertices is computed +by storing the sum of degrees of the vertex pair and subtracting the count of common neighbors, which are double-counted +in the sum of degrees. + +The algorithm first annotates each edge with the endpoint degree. Grouping on the midpoint vertex, each pair of +neighbors is emitted with the endpoint degree sum. Grouping on two-paths, the common neighbors are counted. + +#### Usage +The algorithm takes a simple, undirected graph as input and outputs a `DataSet` of tuples containing two vertex IDs, +the number of common neighbors, and the number of distinct neighbors. The graph ID type must be `Comparable` and --- End diff -- Why doesn't the algorithm return the jaccard index?
--- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. ---