# Guidelines for Experimental Algorithmics in Network Analysis

## TL;DR

This paper proposes universal guidelines and a tool called SimexPal for systematic evaluation of network analysis algorithms, aiming to improve consistency, comparability, and rigor in experimental algorithmics within network science.

## Contribution

It unifies and adapts existing methodologies into comprehensive guidelines and introduces SimexPal, an automated tool for experimental analysis in network analysis research.

## Key findings

- Guidelines enable meaningful comparison of algorithms.
- SimexPal automates experimental procedures and analysis.
- Case study demonstrates effectiveness of the approach.

## Abstract

The field of network science is a highly interdisciplinary area; for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence, research procedures and descriptions of the technical results often differ, sometimes widely. In this paper we focus on methodologies for the experimental part of algorithm engineering for network analysis -- an important ingredient for a research area with empirical focus. More precisely, we unify and adapt existing recommendations from different fields and propose universal guidelines -- including statistical analyses -- for the systematic evaluation of network analysis algorithms. This way, the behavior of newly proposed algorithms can be properly assessed and comparisons to existing solutions become meaningful. Moreover, as the main technical contribution, we provide SimexPal, a highly automated tool to perform and analyze experiments following our guidelines. To illustrate the merits of SimexPal and our guidelines, we apply them in a case study: we design, perform, visualize and evaluate experiments of a recent algorithm for approximating betweenness centrality, an important problem in network analysis. In summary, both our guidelines and SimexPal shall modernize and complement previous efforts in experimental algorithmics; they are not only useful for network analysis, but also in related contexts.