## Adjacency Matrix Aggregationedit

A bucket aggregation returning a form of adjacency matrix.
The request provides a collection of named filter expressions, similar to the `filters`

aggregation
request.
Each bucket in the response represents a non-empty cell in the matrix of intersecting filters.

Given filters named `A`

, `B`

and `C`

the response would return buckets with the following names:

A | B | C | |
---|---|---|---|

| A | A&B | A&C |

| B | B&C | |

| C |

The intersecting buckets e.g `A&C`

are labelled using a combination of the two filter names separated by
the ampersand character. Note that the response does not also include a "C&A" bucket as this would be the
same set of documents as "A&C". The matrix is said to be *symmetric* so we only return half of it. To do this we sort
the filter name strings and always use the lowest of a pair as the value to the left of the "&" separator.

An alternative `separator`

parameter can be passed in the request if clients wish to use a separator string
other than the default of the ampersand.

Example:

PUT /emails/_bulk?refresh { "index" : { "_id" : 1 } } { "accounts" : ["hillary", "sidney"]} { "index" : { "_id" : 2 } } { "accounts" : ["hillary", "donald"]} { "index" : { "_id" : 3 } } { "accounts" : ["vladimir", "donald"]} GET emails/_search { "size": 0, "aggs" : { "interactions" : { "adjacency_matrix" : { "filters" : { "grpA" : { "terms" : { "accounts" : ["hillary", "sidney"] }}, "grpB" : { "terms" : { "accounts" : ["donald", "mitt"] }}, "grpC" : { "terms" : { "accounts" : ["vladimir", "nigel"] }} } } } } }

In the above example, we analyse email messages to see which groups of individuals have exchanged messages. We will get counts for each group individually and also a count of messages for pairs of groups that have recorded interactions.

Response:

{ "took": 9, "timed_out": false, "_shards": ..., "hits": ..., "aggregations": { "interactions": { "buckets": [ { "key":"grpA", "doc_count": 2 }, { "key":"grpA&grpB", "doc_count": 1 }, { "key":"grpB", "doc_count": 2 }, { "key":"grpB&grpC", "doc_count": 1 }, { "key":"grpC", "doc_count": 1 } ] } } }

### Usageedit

On its own this aggregation can provide all of the data required to create an undirected weighted graph.
However, when used with child aggregations such as a `date_histogram`

the results can provide the
additional levels of data required to perform dynamic network analysis
where examining interactions *over time* becomes important.

### Limitationsedit

For N filters the matrix of buckets produced can be N²/2 and so there is a default maximum
imposed of 100 filters . This setting can be changed using the `index.max_adjacency_matrix_filters`

index-level setting.