论文标题
系统网络分析：国家系列的进化和稳定规则
System Network Analytics: Evolution and Stable Rules of a State Series
论文作者
论文摘要
系统进化分析在一个进化的系统上是一个挑战，因为它使状态系列ss = {s1，s2 ... sn}（即，一组按时间订购的状态），几个相互连接的实体随着时间而变化。我们提出了在多个状态中发生的有趣进化规则的稳定特征。我们将其稳定性的进化规则定义为该规则有趣的状态比例。广泛地，我们将稳定规则定义为具有超过给定阈值最小稳定性（MINSTAB）的稳定性的进化规则。我们还定义了持久性度量，这是对持续实体连接的定量度量。我们使用一种用于系统网络分析（SYSNET-ANALYTICS）的方法和算法来解释这一点，该方法使用MINSTAB检索网络进化规则（NERS）和稳定NERS（SNERS）。检索到的信息用于计算提出的系统网络持久性（SNP）度量。这项工作是自动化的，是一种系统网分析工具，可展示对现实世界系统的应用，包括：软件系统，自然语言系统，零售市场系统和IMDB系统。我们量化了系统状态系列中实体连接的稳定性和持久性。这会导致进化信息，这有助于基于知识发现和数据挖掘的系统进化分析。
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.
