Motivo: fast motif counting via succinct color coding and adaptive sampling

TL;DR

Motivo is a scalable, accurate motif counting algorithm for large graphs that leverages novel data structures, biased coloring, and adaptive sampling to efficiently count complex motifs on massive networks.

Contribution

The paper introduces Motivo, a new motif counting algorithm that significantly improves scalability and accuracy through innovative data structures, biased coloring, and adaptive sampling strategies.

Findings

Counts 7-node motifs on graphs with 65M nodes and 1.8B edges in 40 minutes

Accurately counts ~10,000 8-node motifs where previous algorithms fail

Operates efficiently on a high-end desktop machine

Abstract

The randomized technique of color coding is behind state-of-the-art algorithms for estimating graph motif counts. Those algorithms, however, are not yet capable of scaling well to very large graphs with billions of edges. In this paper we develop novel tools for the `motif counting via color coding' framework. As a result, our new algorithm, Motivo, is able to scale well to larger graphs while at the same time provide more accurate graphlet counts than ever before. This is achieved thanks to two types of improvements. First, we design new succinct data structures that support fast common color coding operations, and a biased coloring trick that trades accuracy versus running time and memory usage. These adaptations drastically reduce the time and memory requirements of color coding. Second, we develop an adaptive graphlet sampling strategy, based on a fractional set cover problem, that breaks the additive approximation barrier of standard sampling. This strategy gives multiplicative approximations for all graphlets at once, allowing us to count not only the most frequent graphlets but also extremely rare ones. To give an idea of the improvements, in $40$ minutes Motivo counts $7$-nodes motifs on a graph with $65$M nodes and $1.8$B edges; this is $30$ and $500$ times larger than the state of the art, respectively in terms of nodes and edges. On the accuracy side, in one hour Motivo produces accurate counts of $\approx \! 10.000$ distinct $8$-node motifs on graphs where state-of-the-art algorithms fail even to find the second most frequent motif. Our method requires just a high-end desktop machine. These results show how color coding can bring motif mining to the realm of truly massive graphs using only ordinary hardware.