Complex Network Analysis Using Parallel Approximate Motif Counting

George M. Slota, Kamesh Madduri
2014 2014 IEEE 28th International Parallel and Distributed Processing Symposium  
Subgraph counting forms the basis of many complex network analysis metrics, including motif and anti-motif finding, relative graphlet frequency distance, and graphlet degree distribution agreements. Determining exact subgraph counts is computationally very expensive. In recent work, we present FASCIA, a shared-memory parallel algorithm and implementation for approximate subgraph counting. FASCIA uses a dynamic programming-based approach and is significantly faster than exhaustive enumeration,
more » ... ile generating high-quality approximations of subgraph counts. However, the memory usage of the dynamic programming step prohibits us from applying FASCIA to very large graphs. In this paper, we introduce a distributed-memory parallelization of FASCIA by partitioning the graph and the dynamic programming table. We discuss a new collective communication scheme to make the dynamic programming step memory-efficient. These optimizations enable scaling to much larger networks than before. We also present a simple parallelization strategy for distributed subgraph counting on smaller networks. The new additions let us use subgraph counts as graph signatures for a large network collection, and we analyze this collection using various subgraph count-based graph analytics.
doi:10.1109/ipdps.2014.50 dblp:conf/ipps/SlotaM14 fatcat:uynrdx33p5dtxktuzaypdizphq