This package includs 5 different SimRank implementations: DFS (depth-first search) MapReduce, naive MapReduce, delta MapReduce, matrix multiplication and PageRank-like Random Walk with Restart. You can choose different implementation through configuration.

This implementation is compatible with Spark 0.8.1+ version, you can compile using sbt assembly, before that please configure the correct Hadoop version in build.sbt.

1. Using graph_generate.py to generate random adjacency matrix, you can configure GRAPH_SIZE (number of vertices), EDGE_SIZE (number of edges) to control the matrix rank, this script will serialize matrix to file.
2. Generate initial similarity matrix. Using ./run simrank.SimRankDataPrepare to generate data, it should be noted that two parameters graphASize and graphBSize, which specifies the vertices number of two sub-graphs in the bipartite graph, should be the same as step 1's generated result.
3. Configure config/config.properties and run by ./run simrank.SimRankImpl.

• Step 2 data preparation will generate one initial similarity matrix and one identity matrix, here similarity matrix is a upper triangluar matrix, implementation delta MapReduce will use this matrix as a initial input similarity matrix, for other implementations identity matrix would be enough to use as a initial input similarity matrix, you can skip step 2 if identity matrix is created by yourself.
• Here we focused on matrix multiplication implementation, other implementations are implemented only for reference, may not be well tuned.

This implementation is open sourced under Apache License Version 2.0.