In the previous chapter, we have seen various operations for transforming the elements of a graph and for modifying its structure. Here, we will learn to use a generic and powerful operator named aggregateMessages that is useful for aggregating the neighborhood information of all nodes in the graph. In fact, many graph-processing algorithms rely on iteratively accessing the properties of neighboring nodes and adjacent edges. One such example is the PageRank algorithm.
By applying aggregateMessages to the NCAA College Basketball datasets, you will be able to:
Understand the basic mechanisms and patterns of
aggregateMessagesApply it to create custom graph aggregation operations
Optimize the performance and efficiency of
aggregateMessages