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 twopaths
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 doublecounted
+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 twopaths, 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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