Does GraphFrames api support creation of Bipartite graphs?

Question

Does GraphFrames api support creation of Bipartite graphs in the current version?

Current version: 0.1.0

Spark version : 1.6.1

Answer 1

As pointed out in the comments to this question, neither GraphFrames nor GraphX have built-in support for bipartite graphs. However, they both have more than enough flexibility to let you create bipartite graphs. For a GraphX solution, see this previous answer. That solution uses a shared trait between the different vertex / object type. And while that works with RDDs that's not going to work for DataFrames. A row in a DataFrame has a fixed schema -- it can't sometimes contain a price column and sometimes not. It can have a price column that's sometimes null, but the column has to exist in every row.

Instead, the solution for GraphFrames seems to be that you need to define a DataFrame that's essentially a linear sub-type of both types of objects in your bipartite graph -- it has to contain all of the fields of both types of objects. This is actually pretty easy -- a join with full_outer is going to give you that. Something like this:

val players = Seq(
  (1,"dave", 34),
  (2,"griffin", 44)
).toDF("id", "name", "age")

val teams = Seq(
  (101,"lions","7-1"),
  (102,"tigers","5-3"),
  (103,"bears","0-9")
).toDF("id","team","record")

You could then create a super-set DataFrame like this:

val teamPlayer = players.withColumnRenamed("id", "l_id").join(
  teams.withColumnRenamed("id", "r_id"),
  $"r_id" === $"l_id", "full_outer"
).withColumn("l_id", coalesce($"l_id", $"r_id"))
 .drop($"r_id")
 .withColumnRenamed("l_id", "id")

teamPlayer.show

+---+-------+----+------+------+
| id|   name| age|  team|record|
+---+-------+----+------+------+
|101|   null|null| lions|   7-1|
|102|   null|null|tigers|   5-3|
|103|   null|null| bears|   0-9|
|  1|   dave|  34|  null|  null|
|  2|griffin|  44|  null|  null|
+---+-------+----+------+------+

You could possibly do it a little cleaner with structs:

val tpStructs = players.select($"id" as "l_id", struct($"name", $"age") as "player").join(
  teams.select($"id" as "r_id", struct($"team",$"record") as "team"),
  $"l_id" === $"r_id",
  "full_outer"
).withColumn("l_id", coalesce($"l_id", $"r_id"))
 .drop($"r_id")
 .withColumnRenamed("l_id", "id")

tpStructs.show

+---+------------+------------+
| id|      player|        team|
+---+------------+------------+
|101|        null| [lions,7-1]|
|102|        null|[tigers,5-3]|
|103|        null| [bears,0-9]|
|  1|   [dave,34]|        null|
|  2|[griffin,44]|        null|
+---+------------+------------+

I'll also point out that more or less the same solution would work in GraphX with RDDs. You could always create a vertex via joining two case classes that don't share any traits:

case class Player(name: String, age: Int)
val playerRdd = sc.parallelize(Seq(
  (1L, Player("date", 34)),
  (2L, Player("griffin", 44))
))

case class Team(team: String, record: String)
val teamRdd = sc.parallelize(Seq(
  (101L, Team("lions", "7-1")),
  (102L, Team("tigers", "5-3")),
  (103L, Team("bears", "0-9"))
))

playerRdd.fullOuterJoin(teamRdd).collect foreach println
(101,(None,Some(Team(lions,7-1))))
(1,(Some(Player(date,34)),None))
(102,(None,Some(Team(tigers,5-3))))
(2,(Some(Player(griffin,44)),None))
(103,(None,Some(Team(bears,0-9))))

With all respect to the previous answer, this seems like a more flexible way to handle it -- without having to share a trait between the combined objects.