Github user greghogan commented on a diff in the pull request:
https://github.com/apache/flink/pull/1980#discussion_r63893365
--- 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 --
It does, from `Result.getJaccardIndexScore()`.
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