graph playground

The graph looks like a couple of friends connected to each other:

---+---------+-----+
| id|     name|fraud|
+---+---------+-----+
|  a|    Alice|    1|
|  b|      Bob|    0|
....
|  f|    Fanny|    0|
|  g|    Gabby|    0|
|  h|Fraudster|    1|
+---+---------+-----+

and two different types of edges

+---+---+------------+
|src|dst|relationship|
+---+---+------------+
|  a|  b|        call|
|  b|  c|         sms|
....
|  a|  h|        call|
|  f|  h|        call|
+---+---+------------+

which looks like

goal

Calculate the fraud percentage score for each friend in the social network as the mean of all interactions.

• differenciate by type of interaction
• differenciate by ingoing / outgoing interaction
• weight interactions furhter away i.e. of friends of friends with a smaller weight

In neo4j cyper it could look a bit similar to:

MATCH (a)-->(b)
WHERE b.fraud = 1
RETURN (count() / ( MATCH (a) -->(b) RETURN count() ) * 100)

Task:

• learn pregel
• learn aggregate messages operator :)