# GraphFrames User Guide

This page gives examples of how to use GraphFrames for basic queries, motif finding, and general graph algorithms. This includes code examples in Scala and Python.

- Creating GraphFrames
- Basic graph and DataFrame queries
- Motif finding
- Subgraphs
- Graph algorithms
- Saving and loading GraphFrames
- Message passing via AggregateMessages
- GraphX-GraphFrame conversions

*Note: Most examples use the GraphFrame from the first subsection:
Creating GraphFrames.*

# Creating GraphFrames

Users can create GraphFrames from vertex and edge DataFrames.

*Vertex DataFrame*: A vertex DataFrame should contain a special column named “id” which specifies unique IDs for each vertex in the graph.*Edge DataFrame*: An edge DataFrame should contain two special columns: “src” (source vertex ID of edge) and “dst” (destination vertex ID of edge).

Both DataFrames can have arbitrary other columns. Those columns can represent vertex and edge attributes.

A GraphFrame can also be constructed from a single DataFrame containing edge information. The vertices will be inferred from the sources and destinations of the edges.

The GraphFrame constructed above is available in the GraphFrames package:

The GraphFrame constructed above is available in the GraphFrames package:

# Basic graph and DataFrame queries

GraphFrames provide several simple graph queries, such as node degree.

Also, since GraphFrames represent graphs as pairs of vertex and edge DataFrames, it is easy to make
powerful queries directly on the vertex and edge DataFrames. Those DataFrames are made available
as `vertices`

and `edges`

fields in the GraphFrame.

# Motif finding

Motif finding refers to searching for structural patterns in a graph.

GraphFrame motif finding uses a simple Domain-Specific Language (DSL) for expressing structural
queries. For example, `graph.find("(a)-[e]->(b); (b)-[e2]->(a)")`

will search for pairs of vertices
`a,b`

connected by edges in both directions. It will return a `DataFrame`

of all such
structures in the graph, with columns for each of the named elements (vertices or edges)
in the motif. In this case, the returned columns will be “a, b, e, e2.”

DSL for expressing structural patterns:

- The basic unit of a pattern is an edge.
For example,
`"(a)-[e]->(b)"`

expresses an edge`e`

from vertex`a`

to vertex`b`

. Note that vertices are denoted by parentheses`(a)`

, while edges are denoted by square brackets`[e]`

. - A pattern is expressed as a union of edges. Edge patterns can be joined with semicolons.
Motif
`"(a)-[e]->(b); (b)-[e2]->(c)"`

specifies two edges from`a`

to`b`

to`c`

. - Within a pattern, names can be assigned to vertices and edges. For example,
`"(a)-[e]->(b)"`

has three named elements: vertices`a,b`

and edge`e`

. These names serve two purposes:- The names can identify common elements among edges. For example,
`"(a)-[e]->(b); (b)-[e2]->(c)"`

specifies that the same vertex`b`

is the destination of edge`e`

and source of edge`e2`

. - The names are used as column names in the result
`DataFrame`

. If a motif contains named vertex`a`

, then the result`DataFrame`

will contain a column “a” which is a`StructType`

with sub-fields equivalent to the schema (columns) of`GraphFrame.vertices`

. Similarly, an edge`e`

in a motif will produce a column “e” in the result`DataFrame`

with sub-fields equivalent to the schema (columns) of`GraphFrame.edges`

.

- The names can identify common elements among edges. For example,
- It is acceptable to omit names for vertices or edges in motifs when not needed.
E.g.,
`"(a)-[]->(b)"`

expresses an edge between vertices`a,b`

but does not assign a name to the edge. There will be no column for the anonymous edge in the result`DataFrame`

. Similarly,`"(a)-[e]->()"`

indicates an out-edge of vertex`a`

but does not name the destination vertex. - An edge can be negated to indicate that the edge should
*not*be present in the graph. E.g.,`"(a)-[]->(b); !(b)-[]->(a)"`

finds edges from`a`

to`b`

for which there is*no*edge from`b`

to`a`

.

More complex queries, such as queries which operate on vertex or edge attributes,
can be expressed by applying filters to the result `DataFrame`

.

Many motif queries are stateless and simple to express, as in the examples above.
The next examples demonstrate more complex queries which carry state along a path in the motif.
These queries can be expressed by combining GraphFrame motif finding with filters on the result,
where the filters use sequence operations to construct a series of `DataFrame`

`Column`

s.

For example, suppose one wishes to identify a chain of 4 vertices with some property defined
by a sequence of functions. That is, among chains of 4 vertices `a->b->c->d`

, identify the subset
of chains matching this complex filter:

- Initialize state on path.
- Update state based on vertex
`a`

. - Update state based on vertex
`b`

. - Etc. for
`c`

and`d`

. - If final state matches some condition, then the chain is accepted by the filter.

The below code snippets demonstrate this process, where we identify chains of 4 vertices
such that at least 2 of the 3 edges are “friend” relationships.
In this example, the state is the current count of “friend” edges; in general, it could be any
`DataFrame Column`

.

The above example demonstrated a stateful motif for a fixed-length chain. Currently, in order to search for variable-length motifs, users need to run one query for each possible length. However, the above query patterns allow users to re-use the same code for each length, with the only change being to update the sequence of motif elements (“ab”, “bc”, “cd” above).

# Subgraphs

In GraphX, the `subgraph()`

method takes an edge triplet (edge, src vertex, and dst vertex, plus
attributes) and allows the user to select a subgraph based on triplet and vertex filters.

GraphFrames provide an even more powerful way to select subgraphs based on a combination of motif finding and DataFrame filters.

**Simple subgraph: vertex and edge filters**:
The following example shows how to select a subgraph based upon vertex and edge filters.

**Complex subgraph: triplet filters**:
The following example shows how to select a subgraph based upon triplet filters which
operate on an edge and its src and dst vertices. This example could be extended to go beyond
triplets by using more complex motifs.

# Graph algorithms

GraphFrames provides the same suite of standard graph algorithms as GraphX, plus some new ones. We provide brief descriptions and code snippets below. See the API docs for more details.

Some of the algorithms are currently wrappers around GraphX implementations, so they may not be more scalable than GraphX. More algorithms will be migrated to native GraphFrames implementations in the future.

## Breadth-first search (BFS)

Breadth-first search (BFS) finds the shortest path(s) from one vertex (or a set of vertices) to another vertex (or a set of vertices). The beginning and end vertices are specified as Spark DataFrame expressions.

See Wikipedia on BFS for more background.

For API details, refer to the API docs.

For API details, refer to the API docs.

## Connected components

Computes the connected component membership of each vertex and returns a graph with each vertex assigned a component ID.

See Wikipedia for background.

NOTE: With GraphFrames 0.3.0 and later releases, the default Connected Components algorithm
requires setting a Spark checkpoint directory. Users can revert to the old algorithm using
`.setAlgorithm("graphx")`

.

### Strongly connected components

Compute the strongly connected component (SCC) of each vertex and return a graph with each vertex assigned to the SCC containing that vertex.

See Wikipedia for background.

## Label Propagation Algorithm (LPA)

Run static Label Propagation Algorithm for detecting communities in networks.

Each node in the network is initially assigned to its own community. At every superstep, nodes send their community affiliation to all neighbors and update their state to the mode community affiliation of incoming messages.

LPA is a standard community detection algorithm for graphs. It is very inexpensive computationally, although (1) convergence is not guaranteed and (2) one can end up with trivial solutions (all nodes are identified into a single community).

See Wikipedia for background.

## PageRank

There are two implementations of PageRank.

- The first implementation uses the standalone
`GraphFrame`

interface and runs PageRank for a fixed number of iterations. This can be run by setting`maxIter`

. - The second implementation uses the
`org.apache.spark.graphx.Pregel`

interface and runs PageRank until convergence. This can be run by setting`tol`

.

Both implementations support non-personalized and personalized PageRank, where setting a `sourceId`

personalizes the results for that vertex.

See Wikipedia for background.

## Shortest paths

Computes shortest paths from each vertex to the given set of landmark vertices, where landmarks are specified by vertex ID. Note that this takes edge direction into account.

See Wikipedia for background.

## Triangle count

Computes the number of triangles passing through each vertex.

# Saving and loading GraphFrames

Since GraphFrames are built around DataFrames, they automatically support saving and loading to and from the same set of datasources. Refer to the Spark SQL User Guide on datasources for more details.

The below example shows how to save and then load a graph.

# Message passing via AggregateMessages

Like GraphX, GraphFrames provides primitives for developing graph algorithms. The two key components are:

`aggregateMessages`

: Send messages between vertices, and aggregate messages for each vertex. GraphFrames provides a native`aggregateMessages`

method implemented using DataFrame operations. This may be used analogously to the GraphX API.- joins: Join message aggregates with the original graph.
GraphFrames rely on
`DataFrame`

joins, which provide the full functionality of GraphX joins.

The below code snippets show how to use `aggregateMessages`

to compute the sum of the ages
of adjacent users.

For API details, refer to the API docs.

`AggregateMessages`

does not yet have a Python API, but we expect to add one in the future.

For a more complex example, look at the code used to implement the Belief Propagation example.

# GraphX-GraphFrame conversions

We provide utilities for converting between GraphFrame and GraphX graphs. See the GraphX User Guide for details on GraphX.

## GraphFrame to GraphX

Conversion to GraphX creates a GraphX `Graph`

which has `Long`

vertex IDs and attributes
of type `Row`

.

Vertex and edge attributes are the original rows in `vertices`

and `edges`

, respectively.

Note that vertex (and edge) attributes include vertex IDs (and source, destination IDs) in order to support non-Long vertex IDs. If the vertex IDs are not convertible to Long values, then the values are indexed in order to generate corresponding Long vertex IDs (which is an expensive operation).

The column ordering of the returned `Graph`

vertex and edge attributes are specified by
`GraphFrame.vertexColumns`

and `GraphFrame.edgeColumns`

, respectively.

## GraphX to GraphFrame

GraphFrame provides two conversions methods. The first takes any GraphX graph and converts
the vertex and edge `RDD`

s into `DataFrame`

s using schema inference. Those DataFrames
are then used to create a GraphFrame.

The second conversion method is more complex and is useful for users with existing GraphX code. Its main purpose is to support workflows of the following form: (1) convert a GraphFrame to GraphX, (2) run GraphX code to augment the GraphX graph with new vertex or edge attributes, and (3) merge the new attributes back into the original GraphFrame.

For example, given:

- GraphFrame
`originalGraph`

- GraphX
`Graph[String, Int]`

`graph`

with a String vertex attribute we want to call “category” and an Int edge attribute we want to call “count”

We can call `fromGraphX(originalGraph, graph, Seq("category"), Seq("count"))`

to produce
a new GraphFrame. The new GraphFrame will be an augmented version of `originalGraph`

,
with new `GraphFrame.vertices`

column “category” and new `GraphFrame.edges`

column “count” added.

For example usage, look at the code used to implement the Belief Propagation example.

## Example conversions

The below example demonstrates simple GraphFrame-GraphX conversions.

For API details, refer to the API docs for:

These conversions are only supported in Scala since GraphX does not have a Python API.