Fast parallel conversion of edge list to adjacency list for large-scale graphs

In the era of bigdata, we are deluged with massive graph data emerged from numerous social and scientific applications. In most cases, graph data are generated as lists of edges (edge list), where an edge denotes a link between a pair of entities. However, most of the graph algorithms work efficiently when information of the adjacent nodes (adjacency list) for each node are readily available. Although the conversion from edge list to adjacency list can be trivially done on the fly for small graphs, such conversion becomes challenging for the emerging large-scale graphs consisting billions of nodes and edges. These graphs do not fit into the main memory of a single computing machine and thus require distributed-memory parallel or external-memory algorithms. In this paper, we present efficient MPI-based distributed memory parallel algorithms for converting edge lists to adjacency lists. To the best of our knowledge, this is the first work on this problem. To address the critical load balancing issue, we present a parallel load balancing scheme which improves both time and space efficiency significantly. Our fast parallel algorithm works on massive graphs, achieves very good speedups, and scales to large number of processors. The algorithm can convert an edge list of a graph with 20 billion edges to the adjacency list in less than 2 minutes using 1024 processors. Denoting the number of nodes, edges and processors by n, m, and P, respectively, the time complexity of our algorithm is O(P + n+P) which provides a speedup factor of at least Ω(min{P,davg}), where davg is the average degree of the nodes. The algorithm has a space complexity of O(P ), which is optimal.