[role="xpack"]
[[xpack-graph]]
= Graphing Connections in Your Data
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<titleabbrev>Graph</titleabbrev>
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[partintro]
--
The {graph-features} enable you to discover how items in an
Elasticsearch index are related. You can explore the connections between
indexed terms and see which connections are the most meaningful. This can be
useful in a variety of applications, from fraud detection to recommendation
engines.

For example, graph exploration could help you uncover website vulnerabilities
that hackers are targeting so you can harden your website. Or, you might
provide graph-based personalized recommendations to your e-commerce customers.

The {graph-features} provide a simple, yet powerful {ref}/graph-explore-api.html[graph exploration API],
and an interactive graph visualization tool for Kibana. Both work out of the
box with existing Elasticsearch indices--you don't need to store any
additional data to use these features.

[[how-graph-works]]
[float]
=== How Graphs Work
The Graph API provides an alternative way to extract and summarize information
about the documents and terms in your Elasticsearch index. A _graph_ is really
just a network of related items. In our case, this means a network of related
terms in the index.

The terms you want to include in the graph are called _vertices_. The
relationship between any two vertices is a _connection_. The connection
summarizes the documents that contain both vertices' terms.

image::graph/images/graph-vertices-connections.jpg["Graph components"]

NOTE: If you're into https://en.wikipedia.org/wiki/Graph_theory[graph theory],
you might know vertices and connections as _nodes_ and _edges_. They're the
same thing, we just want to use terminology that makes sense to people who
aren't graph geeks and avoid any confusion with the nodes in an Elasticsearch
cluster.

The graph vertices are simply the terms that you've already indexed. The
connections are derived on the fly using Elasticsearch aggregations. To
identify the most _meaningful_ connections, the Graph API leverages
Elasticsearch relevance scoring. The same data structures and relevance ranking
tools built into Elasticsearch to support text searches enable the Graph API to
separate useful signals from the noise that is typical of most connected data.

This foundation lets you easily answer questions like:

* What are the shared behaviors of people trying to hack my website?
* If users bought this type of gardening glove, what other products might they
be interested in?
* Which people on Stack Overflow have expertise in both Hadoop-related
technologies and Python-related tech?

But what about performance, you ask? The Elasticsearch aggregation framework
enables the Graph API to quickly summarize millions of documents as a single
super-connection. Instead of retrieving every banking transaction between
accounts A and B, it derives a single connection that represents that
relationship. And, of course, this summarization process works across
multi-node clusters and scales with your Elasticsearch deployment.
Advanced options let you control how your data is sampled and summarized.
You can also set timeouts to prevent graph queries from adversely
affecting the cluster.
--

include::getting-started.asciidoc[]

include::configuring-graph.asciidoc[]

include::troubleshooting.asciidoc[]

include::limitations.asciidoc[]
