Strongly Connected Components in Stream Graphs: Computation and Experimentations

TL;DR

This paper introduces algorithms for computing strongly connected components in stream graphs, evaluates their performance through experiments, and proposes an approximation method to reduce computational costs and enhance dataset insights.

Contribution

The paper provides the first algorithms for strongly connected components in stream graphs, along with an implementation and experimental comparison, plus an approximation scheme for efficiency.

Findings

Algorithms with polynomial complexity for stream graph SCCs

Experimental comparison across diverse practical cases

Approximation scheme reduces computation and offers dataset insights

Abstract

Stream graphs model highly dynamic networks in which nodes and/or links arrive and/or leave over time. Strongly connected components in stream graphs were defined recently, but no algorithm was provided to compute them. We present here several solutions with polynomial time and space complexities, each with its own strengths and weaknesses. We provide an implementation and experimentally compare the algorithms in a wide variety of practical cases. In addition, we propose an approximation scheme that significantly reduces computation costs, and gives even more insight on the dataset.