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System Evolution Analytics on a system that evolves is a challenge because itmakes a State Series SS = {S1, S2... SN} (i.e., a set of states ordered bytime) with several inter-connected entities changing over time. We presentstability characteristics of interesting evolution rules occurring in multiplestates. We defined an evolution rule with its stability as the fraction ofstates in which the rule is interesting. Extensively, we defined stable rule asthe evolution rule having stability that exceeds a given threshold minimumstability (minStab). We also defined persistence metric, a quantitative measureof persistent entity-connections. We explain this with an approach andalgorithm for System Network Analytics (SysNet-Analytics), which uses minStabto retrieve Network Evolution Rules (NERs) and Stable NERs (SNERs). Theretrieved information is used to calculate a proposed System NetworkPersistence (SNP) metric. This work is automated as a SysNet-Analytics Tool todemonstrate application on real world systems including: software system,natural-language system, retail market system, and IMDb system. We quantifiedstability and persistence of entity-connections in a system state series. Thisresults in evolution information, which helps in system evolution analyticsbased on knowledge discovery and data mining.

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