4.3. Jaccard Similarity

The single-source version of this problem is:

Given a specific vertex A, find other vertices B that are most similar to A.

This is done by computing a similarity metric for each vertex B and then rank-ordering the vertices by that score.

The Jaccard similarity metric is between two different vertices A and B. It is computed as the ratio of the intersection of their neighboring U vertex sets to the union of their neighbor sets. The diagram shows, for a given A and B, a neighborhood union of size 8. The intersection of the two neighborhoods include: {1, 3, 7}, so the intersection size is 3. If the intersection is the same size as the union, that means the two vertices have identical neighborhoods and are therefore considered to be most similar.

Two Vertices with Similar Neighborhoods

Two Vertices with Similar Neighborhoods

This metric is based on the graph topology and is not influenced by the vertex and edge property values. It may be more interesting to include data values as part of a Jaccard analysis.

The overall flow of this analysis using xGT involves these phases:

  1. Build a connectivity graph to drastically reduce the search space

  2. Compute the Jaccard Scores

4.3.1. Connect to the xGT Server

Before embarking on phase 1, some prelimiaries are required to interact with the xGT server.

import xgt
import getpass
server=xgt.Connection(auth = xgt.BasicAuth(getpass.getuser(),
                                           getpass.getpass()))
server.server_version

  ········

  '1.8.0'

4.3.2. Retrieve Metadata about the Graph

Select specific parts of the graph to analyze:

  • A_VERTEX_KEY selects the key of the unique vertex to play the role of A. Note that this vertex is the single source and the Jaccard computation will find other vertices most similar to A.

  • EDGE_FRAME_NAME is the name of the edge frame connecting A to all of the B vertices.

From the selected information, the components of the graph in the xGT server are extracted.

A_VERTEX_KEY='VPN'
EDGE_FRAME_NAME='Netflow'

server.set_default_namespace('lanl')

edge = server.get_frame(EDGE_FRAME_NAME)
source_vertex = server.get_frame(edge.source_name)
target_vertex = server.get_frame(edge.target_name)
VERTEX_KEY = source_vertex.key

(source_vertex, edge, target_vertex, VERTEX_KEY)

  (<xgt.graph.VertexFrame at 0x111686580>,
   <xgt.graph.EdgeFrame at 0x111686c40>,
   <xgt.graph.VertexFrame at 0x1116868b0>,
   'device')

4.3.3. Define Utility Functions

These utility functions are used during the phases to interact with the xGT server.

# Utility to print the data sizes currently in xGT
def print_data_summary(server):
    frames = (server.get_frames())
    if len(frames)==0:
        print("No data has been defined")
    for frame in frames:
        frame_name = frame.name
        if isinstance(frame, xgt.EdgeFrame):
            frame_type = 'edge'
        elif isinstance(frame, xgt.VertexFrame):
            frame_type = 'vertex'
        elif isinstance(frame, xgt.TableFrame):
            frame_type = 'table'
        else:
            frame_type = 'unknown'
        print("{} ({}): {:,}".format(frame_name, frame_type, frame.num_rows))

print_data_summary(server)

  lanl__Devices (vertex): 157,875
  lanl__Netflow (edge): 222,323,503

4.3.4. Phase 1: Build a Connectivity Graph

Between any two vertices, a -> b, there may be many edges, perhaps millions.

To compute Jaccard similarity, all we need to know is if there is at least one or if there are zero edges.

We superimpose a connectivity graph on the same vertex set and leave either zero or one edges between every pair of vertices.

This phase consists of finding distinct vertex pairs with at least one edge. Note that this results in a table with the source and target vertex keys. From there we populate a new edge frame.

# create connectivity edge
connected_to_edge = 'ConnectedTo'
server.drop_frame(connected_to_edge)
edge_schema = dict(edge.schema)
connected_to = server.create_edge_frame(
    name=connected_to_edge,
    schema=[[edge.source_key, edge_schema[edge.source_key]],
            [edge.target_key, edge_schema[edge.target_key]]],
    source=edge.source_name,
    target=edge.target_name,
    source_key=edge.source_key,
    target_key=edge.target_key)
connected_to

  <xgt.graph.EdgeFrame at 0x111188af0>

%%time
# Find distinct pairs of vertices over edge frame, create one edge for each distinct pair
q = """
MATCH (a)-[e1:{edge_frame}]->(b)
WHERE unique_vertices(a, b)
WITH DISTINCT a, b
CREATE (a)-[e: {connected_to_edge} {{ }}]->(b)
RETURN count(*) AS count
""".format(edge_frame=EDGE_FRAME_NAME,
           connected_to_edge=connected_to_edge
          )
print("query:\n\n{}".format(q))
job = server.run_job(q, parameters={})
print("Number of 'connected_to' edges: {:,}".format(job.get_data()[0][0]))

  query:


  MATCH (a)-[e1:Netflow]->(b)
  WHERE unique_vertices(a, b)
  WITH DISTINCT a, b
  CREATE (a)-[e: ConnectedTo { }]->(b)
  RETURN count(*) AS count

  Number of 'connected_to' edges: 559,680
  CPU times: user 5.85 ms, sys: 6.31 ms, total: 12.2 ms
  Wall time: 9.13 s

4.3.5. Phase 2: Compute the Jaccard scores

This phase finds two-paths from the A vertex to all other B vertices through an intermediate vertex U.

From this information, for each vertex B, the intersection size and union size are computed followed by the ratio (which is the Jaccard score).

We rely on the observation that the outdegree sum is the same as the size of the union of A and B neighborhoods.

We also rely on the observation that the number of two-paths from A to B is the same as the size of the intersection of the two neighborhoods.

%%time
import pandas
q = """
MATCH (a)-[e1:{connected_to_edge}]->(u)<-[e2:{connected_to_edge}]-(b)
WHERE unique_vertices(a, b, u)
  AND a.{vertex_key} = $a_vertex_key
WITH DISTINCT b, u,
     outdegree(a, {connected_to_edge}) + outdegree(b, {connected_to_edge}) AS out_degree_sum
WITH b, out_degree_sum, count(*) AS intersection_size
WITH b, intersection_size, out_degree_sum - intersection_size AS union_size
RETURN
    b.{vertex_key} AS b,
    intersection_size,
    union_size,
    toFloat(intersection_size) / union_size AS jaccard_score
ORDER BY jaccard_score DESC
LIMIT 50
""".format(connected_to_edge=connected_to_edge, vertex_key=source_vertex.key)

print("query:\n\n{}".format(q))
job = server.run_job(q, parameters={"a_vertex_key":A_VERTEX_KEY})

df=job.get_data(format='pandas')
df

  query:


  MATCH (a)-[e1:ConnectedTo]->(u)<-[e2:ConnectedTo]-(b)
  WHERE unique_vertices(a, b, u)
    AND a.device = $a_vertex_key
  WITH DISTINCT b, u,
       outdegree(a, ConnectedTo) + outdegree(b, ConnectedTo) AS out_degree_sum
  WITH b, out_degree_sum, count(*) AS intersection_size
  WITH b, intersection_size, out_degree_sum - intersection_size AS union_size
  RETURN
      b.device AS b,
      intersection_size,
      union_size,
      toFloat(intersection_size) / union_size AS jaccard_score
  ORDER BY jaccard_score DESC
  LIMIT 50

  CPU times: user 538 ms, sys: 260 ms, total: 798 ms
  Wall time: 2.8 s
b intersection_size union_size jaccard_score
0 Comp678443 87 665 0.130827
1 Comp030334 74 781 0.094750
2 Comp965575 72 780 0.092308
3 Comp257274 72 782 0.092072
4 Comp866402 72 783 0.091954
5 Comp211931 50 580 0.086207
6 Comp769797 50 584 0.085616
7 Comp148004 54 637 0.084772
8 Comp148684 49 580 0.084483
9 Comp464369 49 583 0.084048
10 Comp889106 50 598 0.083612
11 Comp415532 48 586 0.081911
12 Comp501848 48 586 0.081911
13 Comp725432 48 592 0.081081
14 Comp304006 47 583 0.080617
15 Comp468329 47 583 0.080617
16 Comp055001 47 585 0.080342
17 Comp211432 46 582 0.079038
18 Comp377866 46 584 0.078767
19 Comp507194 46 584 0.078767
20 Comp613192 46 585 0.078632
21 Comp771132 46 586 0.078498
22 Comp599337 46 587 0.078365
23 Comp719990 45 582 0.077320
24 Comp643534 46 595 0.077311
25 Comp881722 45 583 0.077187
26 Comp493395 45 583 0.077187
27 Comp614801 45 585 0.076923
28 Comp764436 45 586 0.076792
29 Comp302339 45 592 0.076014
30 Comp991220 46 608 0.075658
31 Comp130208 44 582 0.075601
32 Comp924691 45 598 0.075251
33 Comp018032 44 585 0.075214
34 Comp943564 44 585 0.075214
35 Comp498377 44 590 0.074576
36 Comp210768 44 592 0.074324
37 Comp688526 43 581 0.074010
38 Comp958822 43 581 0.074010
39 Comp591316 43 582 0.073883
40 Comp856137 43 582 0.073883
41 Comp354119 43 582 0.073883
42 Comp617264 43 582 0.073883
43 Comp498743 43 583 0.073756
44 Comp559728 43 584 0.073630
45 Comp084630 44 598 0.073579
46 Comp356840 43 585 0.073504
47 Comp077821 43 585 0.073504
48 Comp452053 43 585 0.073504
49 Comp959644 43 587 0.073254 
server.drop_frame(connected_to_edge)

  True

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