Fast and scalable connected component computation
Finding connected components in a graph is a well-known problem in a wide variety of application areas such as social network analysis, data mining, image processing, and etc. We present an efficient and scalable approach to find all the connected components in a given graph. We compare our approach with the state-of-the-art on a real-world graph. We also demonstrate the viability of our approach on a massive graph with ˜6B nodes and ˜92B edges on an 80-node Hadoop cluster. To the best of our knowledge, this is the largest graph publicly used in such an experiment.
1. A data processing system for finding connected components in a graph comprising:
an input device that receives a list of edges in the graph; and
a distributed processing arrangement coupled to the input device, the distributed processing arrangement including a plurality of processors operatively coupled to at least one memory that execute, in a distributed fashion, an iterative map and reduce process that generates adjacency for nodes in the graph;
wherein the distributed processing arrangement is configured to map connected components in the graph without storing the entire connected components in the at least one memory,
wherein the distributed processing arrangement uses the smallest node identifier in each connected component as the identifier of that component and the output comprises a mapping table from each node in the graph to the smallest node ID in the corresponding connected component.
2. The system of claim 1 wherein the distributed processing arrangement comprises MapReduce.
3. The system of claim 1 wherein the distributed processing arrangement comprises Hadoop.
4. The system of claim 1 wherein the distributed processing arrangement chains the iterative generation of adjacency and the deduplication so that both run iteratively until the corresponding component identifiers for all nodes in the graph are found.
5. The system of claim 1 wherein the distributed processing arrangement passes values to be deduplicated in a sorted way with custom partitioning.
6. The system of claim 1 wherein the distributed processing arrangement finds all connected components in the graph without loading all of said connected components into the memory for simultaneous storage in the memory.
7. The system of claim 1 wherein the distributed processing arrangement is configured to apply mappers to all input key-value pairs to generate an arbitrary number of intermediate key-value pairs, and apply reducers to all values associated with the same key.
8. The system of claim 7 wherein the distributed processing arrangement is configured to write output key-value pairs from each reducer stage into a distributed file system to provide r files where r is the number of reducers.
9. The system of claim 1 wherein the distributed processing arrangement is configured to assign each map task a sequence of input key value pairs.
10. The system of claim 1 wherein the distributed processing arrangement is configured to supply reducers with values in an unsorted order.
11. The system of claim 1 wherein the distributed processing arrangement is configured to iterate values just once without loading all of the iterate values into the at least one memory.