The theme of this paper is how to find all instances of a given “sample” graph in a larger “data graph,” using a single round of map-reduce. For the simplest sample graph, the triangle, we improve upon the best known such algorithm. We then examine the general case, considering both the communication cost between mappers and reducers and the total computation cost at the reducers. To minimize communication cost, we exploit the techniques of  for computing multiway joins (evaluating conjunctive queries) in a single map-reduce round. Several methods are shown for translating sample graphs into a union of conjunctive queries with as few queries as possible. We also address the matter of optimizing computation cost. Many serial algorithms are shown to be “convertible,” in the sense that it is possible to partition the data graph, explore each partition in a separate reducer, and have the total computation cost at the reducers be of the same order as the computation cost of the serial algorithm.
Bibtex: Afrati et al. (2013)
Foto N Afrati, Dimitris Fotakis, and Jeffrey D Ullman. Enumerating subgraph instances using map-reduce. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on, 62–73. IEEE, 2013. ↩