Flow Motifs in Interaction Networks

TL;DR

This paper introduces network flow motifs, a new way to identify significant flow transfer patterns in interaction networks, with scalable algorithms tested on real datasets showing their prevalence over random networks.

Contribution

The paper presents a novel concept of flow motifs and algorithms for their detection, including top-k and maximum flow instances, in large dynamic interaction networks.

Findings

Flow motifs are more frequent in real networks than in random ones.

The proposed algorithms are scalable to large datasets.

Flow motifs reveal significant transfer patterns in various real-world networks.

Abstract

Many real-world phenomena are best represented as interaction networks with dynamic structures (e.g., transaction networks, social networks, traffic networks). Interaction networks capture flow of data which is transferred between their vertices along a timeline. Analyzing such networks is crucial toward comprehend- ing processes in them. A typical analysis task is the finding of motifs, which are small subgraph patterns that repeat themselves in the network. In this paper, we introduce network flow motifs, a novel type of motifs that model significant flow transfer among a set of vertices within a constrained time window. We design an algorithm for identifying flow motif instances in a large graph. Our algorithm can be easily adapted to find the top-k instances of maximal flow. In addition, we design a dynamic programming module that finds the instance with the maximum flow. We evaluate the performance of the algorithm on three real datasets and identify flow motifs which are significant for these graphs. Our results show that our algorithm is scalable and that the real networks indeed include interesting motifs, which appear much more frequently than in randomly generated networks having similar characteristics.