Package 'graphframes'

Breadth-first search (BFS)

Description

Breadth-first search (BFS)

Usage

gf_bfs(x, from_expr, to_expr, max_path_length = 10, edge_filter = NULL,
  ...)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
from_expr	Spark SQL expression specifying valid starting vertices for the BFS.
to_expr	Spark SQL expression specifying valid target vertices for the BFS.
max_path_length	Limit on the length of paths.
edge_filter	Spark SQL expression specifying edges which may be used in the search.
...	Optional arguments, currently not used.

Examples

## Not run: 
g <- gf_friends(sc)
gf_bfs(g, from_expr = "name = 'Esther'", to_expr = "age < 32")

## End(Not run)

---

Cache the GraphFrame

Description

Cache the GraphFrame

Usage

gf_cache(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

Chain graph

Description

Returns a chain graph of the given size with Long ID type. The vertex IDs are 0, 1, ..., n-1, and the edges are (0, 1), (1, 2), ...., (n-2, n-1).

Usage

gf_chain(sc, n)

Arguments

sc	A Spark connection.
n	Size of the graph to return.

Examples

## Not run: 
gf_chain(sc, 5)

## End(Not run)

---

Connected components

Description

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

Usage

gf_connected_components(x, broadcast_threshold = 1000000L,
  algorithm = c("graphframes", "graphx"), checkpoint_interval = 2L,
  ...)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
broadcast_threshold	Broadcast threshold in propagating component assignments.
algorithm	One of 'graphframes' or 'graphx'.
checkpoint_interval	Checkpoint interval in terms of number of iterations.
...	Optional arguments, currently not used.

Examples

## Not run: 
# checkpoint directory is required for gf_connected_components()
spark_set_checkpoint_dir(sc, tempdir())
g <- gf_friends(sc)
gf_connected_components(g)

## End(Not run)

---

Degrees of vertices

Description

Degrees of vertices

Usage

gf_degrees(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

Edges column names

Description

Edges column names

Usage

gf_edge_columns(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

Extract edges DataFrame

Description

Extract edges DataFrame

Usage

gf_edges(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

Motif finding: Searching the graph for structural patterns

Description

Motif finding uses a simple Domain-Specific Language (DSL) for expressing structural queries. For example, gf_find(g, "(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 in order of the pattern: "a, e, b, e2."

Usage

gf_find(x, pattern)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
pattern	pattern specifying a motif to search for

Examples

## Not run: 
gf_friends(sc) %>%
  gf_find("(a)-[e]->(b); (b)-[e2]->(a)")

## End(Not run)

---

Graph of friends in a social network.

Description

Graph of friends in a social network.

Usage

gf_friends(sc)

Arguments

sc	A Spark connection.

Examples

## Not run: 
library(sparklyr)
sc <- spark_connect(master = "local")
gf_friends(sc)

## End(Not run)

---

Create a new GraphFrame

Description

Create a new GraphFrame

Usage

gf_graphframe(vertices = NULL, edges)

Arguments

vertices	A tbl_spark representing vertices.
edges	A tbl_psark representing edges.

Examples

## Not run: 
library(sparklyr)
sc <- spark_connect(master = "local", version = "2.3.0")
v_tbl <- sdf_copy_to(
  sc, data.frame(id = 1:3, name = LETTERS[1:3])
)
e_tbl <- sdf_copy_to(
  sc, data.frame(src = c(1, 2, 2), dst = c(2, 1, 3),
                 action = c("love", "hate", "follow"))
)
gf_graphframe(v_tbl, e_tbl)
gf_graphframe(edges = e_tbl)

## End(Not run)

---

Generate a grid Ising model with random parameters

Description

Generate a grid Ising model with random parameters

Usage

gf_grid_ising_model(sc, n, v_std = 1, e_std = 1)

Arguments

sc	A Spark connection.
n	Length of one side of the grid. The grid will be of size n x n.
v_std	Standard deviation of normal distribution used to generate vertex factors "a". Default of 1.0.
e_std	Standard deviation of normal distribution used to generate edge factors "b". Default of 1.0.

Details

This method generates a grid Ising model with random parameters. Ising models are probabilistic graphical models over binary variables xi. Each binary variable xi corresponds to one vertex, and it may take values -1 or +1. The probability distribution P(X) (over all xi) is parameterized by vertex factors ai and edge factors bij:

$P(X) = (1/Z) * exp[ \sum_i a_i x_i + \sum_{ij} b_{ij} x_i x_j ]$

Value

GraphFrame. Vertices have columns "id" and "a". Edges have columns "src", "dst", and "b". Edges are directed, but they should be treated as undirected in any algorithms run on this model. Vertex IDs are of the form "i,j". E.g., vertex "1,3" is in the second row and fourth column of the grid.

Examples

## Not run: 
gf_grid_ising_model(sc, 5)

## End(Not run)

---

In-degrees of vertices

Description

In-degrees of vertices

Usage

gf_in_degrees(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

Label propagation algorithm (LPA)

Description

Run static Label Propagation for detecting communities in networks. Each node in the network is initially assigned to its own community. At every iteration, 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).

Usage

gf_lpa(x, max_iter, ...)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
max_iter	Maximum number of iterations.
...	Optional arguments, currently not used.

Examples

## Not run: 
g <- gf_friends(sc)
gf_lpa(g, max_iter = 5)

## End(Not run)

---

Out-degrees of vertices

Description

Out-degrees of vertices

Usage

gf_out_degrees(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

PageRank

Description

PageRank

Usage

gf_pagerank(x, tol = NULL, reset_probability = 0.15, max_iter = NULL,
  source_id = NULL, ...)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
tol	Tolerance.
reset_probability	Reset probability.
max_iter	Maximum number of iterations.
source_id	(Optional) Source vertex for a personalized pagerank.
...	Optional arguments, currently not used.

Examples

## Not run: 
g <- gf_friends(sc)
gf_pagerank(g, reset_probability = 0.15, tol = 0.01)

## End(Not run)

---

Persist the GraphFrame

Description

Persist the GraphFrame

Usage

gf_persist(x, storage_level = "MEMORY_AND_DISK")

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
storage_level	The storage level to be used. Please view the Spark Documentation for information on what storage levels are accepted.

---

Register a GraphFrame object

Description

Register a GraphFrame object

Usage

gf_register(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

Strongly connected components

Description

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

Usage

gf_scc(x, max_iter, ...)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
max_iter	Maximum number of iterations.
...	Optional arguments, currently not used.

Examples

## Not run: 
g <- gf_friends(sc)
gf_scc(g, max_iter = 10)

## End(Not run)

---

Shortest paths

Description

Computes shortest paths from every vertex to the given set of landmark vertices. Note that this takes edge direction into account.

Usage

gf_shortest_paths(x, landmarks, ...)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
landmarks	IDs of landmark vertices.
...	Optional arguments, currently not used.

Examples

## Not run: 
g <- gf_friends(sc)
gf_shortest_paths(g, landmarks = c("a", "d"))

## End(Not run)

---

Generate a star graph

Description

Returns a star graph with Long ID type, consisting of a central element indexed 0 (the root) and the n other leaf vertices 1, 2, ..., n.

Usage

gf_star(sc, n)

Arguments

sc	A Spark connection.
n	The number of leaves.

Examples

## Not run: 
gf_star(sc, 5)

## End(Not run)

---

Computes the number of triangles passing through each vertex.

Description

This algorithm ignores edge direction; i.e., all edges are treated as undirected. In a multigraph, duplicate edges will be counted only once.

Usage

gf_triangle_count(x, ...)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
...	Optional arguments, currently not used.

Examples

## Not run: 
g <- gf_friends(sc)
gf_triangle_count(g)

## End(Not run)

---

Triplets of graph

Description

Triplets of graph

Usage

gf_triplets(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

Generate two blobs

Description

Two densely connected blobs (vertices 0->n-1 and n->2n-1) connected by a single edge (0->n).

Usage

gf_two_blobs(sc, blob_size)

Arguments

sc	A Spark connection.
blob_size	The size of each blob.

Examples

## Not run: 
gf_two_blobs(sc, 3)

## End(Not run)

---

Unpersist the GraphFrame

Description

Unpersist the GraphFrame

Usage

gf_unpersist(x, blocking = FALSE)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
blocking	whether to block until all blocks are deleted

---

Vertices column names

Description

Vertices column names

Usage

gf_vertex_columns(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

Extract vertices DataFrame

Description

Extract vertices DataFrame

Usage

gf_vertices(x)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).

---

Retrieve a GraphFrame

Description

Retrieve a GraphFrame

Usage

spark_graphframe(x, ...)

spark_graphframe(x, ...)

Arguments

x	An object coercable to a GraphFrame (typically, a gf_graphframe).
...	additional arguments, not used

---