Understand composition of a graph in Spark GraphX.
Being able to create a graph.
Being able to use the built-in graph algorithm.
In this section we begin by creating a graph with patient and diagnostic codes. Later we will show how to run graph algorithms on the the graph you will create.
Spark GraphX abstracts a graph as a concept named Property Graph, which means that each edge and vertex is associated with some properties. The Graph class has the following definition
Where VD and ED define property types of each vertex and edge respectively. We can regard VertexRDD[VD] as RDD of (VertexID, VD) tuple and EdgeRDD[ED] as RDD of (VertexID, VertexID, ED).
Let's create a graph of patients and diagnostic codes. For each patient we can assign its patient id as vertex property, and for each diagnostic code, we will use the code as vertex property. For the edge between patient and diagnostic code, we will use number of times the patient is diagnosed with given disease as edge property.
Let's first define necessary data structure and import
Load raw data
Load patient event data and filter out diagnostic related events only
Let's create patient vertex
In order to use the newly created vetext id, we finally collect all the patient to VertrexID mapping.
Theoretically, collecting RDD to driver is not an efficient practice. One can obtain uniqueness of ID by calculating ID directly with a Hash.
Diagnostic code vertex
Similar to patient vertex, we can create diagnostic code vertex with
Here we assign vertex id by adding the result of zipWithIndex with an offset obtained from previous patient vertex to avoid ID confliction between patient and diagnostic code.
In order to create edge, we will need to know vertext id of vertices we just created.
We first broadcast patient and diagnostic code to vertext id mapping. Broadcast can avoid unnecessary copy in distributed setting thus will be more effecient. Then we count occurrence of (patient-id, icd-9-code) pairs with map and reduceByKey, finally we translate them to proper VertexID.
Assemble vetex and edge
We will need to put vertices and edges together to create the graph
Given the graph we created, we can run some basic graph operations.
Connected component can help find disconnected subgraphs. GraphX provides the API to get connected components as below
The return result is a graph and assigned components of original graph is stored as VertexProperty. For example
The first element of the tuple is VertexID identical to original graph. The second element in the tuple is connected component represented by the lowest-numbered VertexID in that component. In above example, five vertices belong to same component.
We can easily get number of connected components using operations on RDD as below.
The property graph abstraction of GraphX is a directed graph. It provides computation of in-dgree, out-degree and total degree. For example, we can get degrees as
GraphX also provides implementation of the famous PageRank algorithm, which can compute the 'importance' of a vertex. The graph we generated above is a bipartite graph and not suitable for PageRank. To gve an example of PageRank, we randomly generate a graph and run fixed iteration of PageRank algorithm on it.
Or, we can run PageRank until converge with tolerance as 0.01 using randomGraph.pageRank(0.01)
Next, we show some how we can ultilize the graph operations to solve some practical problems in the healthcare domain.
Comorbidity is additional disorders co-occuring with primary disease. We know all the case patients have heart failure, we can explore possible comorbidities as below (see comments for more explaination)
And we can check the vertex of index 3129 in original graph is
The 4019 code correponds to Hypertension, which is reasonable.
Given a patient diagnostic graph, we can also find similar patients. One of the most straightforward approach is shortest path on the graph.