System Network Complexity: Network Evolution Subgraphs of System State Series

Era of computation intelligence leads to various kinds of systems that evolve. Usually, an evolving system contains evolving interconnected entities (or components) that make evolving networks for the system State Series SS = {S₁, S₂ . . . SN} created over time, where Si represents the ith system state. In this paper, we introduce an approach for mining Network Evolution Subgraphs such as Network Evolution Graphlets (NEGs) and Network Evolution Motifs (NEMs) from a set of evolving networks. We used graphlets information of a state to calculate System State Complexity (SSC). The System State Complexities (SSCs) represent time-varying complexities of multiple states. Additionally, we also used the NEGs information to calculate Evolving System Complexity (ESC) for a state series over time. We proposed an algorithm named System Network Complexity (SNC) for mining NEGs, SSCs, and ESC, which analyzes a pre-evolved state series of an evolving system. We prototyped the technique as a tool named SNC-Tool, which is applied to six real-world evolving systems collected from open-internet repositories of four different domains: software system, natural language system, retail market basket system, and IMDb movie genres system. This is demonstrated as experimentation reports containing retrieved-NEGs, NEMs, SSCs, and ESC- for each evolving system.