System Network Analytics: Evolution and Stable Rules of a State Series

TL;DR

This paper introduces a novel framework for analyzing the evolution of complex systems over time by defining stability and persistence metrics, and demonstrates its application across various real-world systems.

Contribution

It proposes a new approach and algorithms for System Network Analytics that identify stable evolution rules and measure persistence in evolving systems.

Findings

Identified stable evolution rules in multiple real-world systems.

Quantified entity-connection persistence over system states.

Demonstrated the effectiveness of the approach in diverse domains.

Abstract

System Evolution Analytics on a system that evolves is a challenge because it makes a State Series SS = {S1, S2... SN} (i.e., a set of states ordered by time) with several inter-connected entities changing over time. We present stability characteristics of interesting evolution rules occurring in multiple states. We defined an evolution rule with its stability as the fraction of states in which the rule is interesting. Extensively, we defined stable rule as the evolution rule having stability that exceeds a given threshold minimum stability (minStab). We also defined persistence metric, a quantitative measure of persistent entity-connections. We explain this with an approach and algorithm for System Network Analytics (SysNet-Analytics), which uses minStab to retrieve Network Evolution Rules (NERs) and Stable NERs (SNERs). The retrieved information is used to calculate a proposed System Network Persistence (SNP) metric. This work is automated as a SysNet-Analytics Tool to demonstrate application on real world systems including: software system, natural-language system, retail market system, and IMDb system. We quantified stability and persistence of entity-connections in a system state series. This results in evolution information, which helps in system evolution analytics based on knowledge discovery and data mining.