Abstract: In data analysis,concept-cognitive learning in networks is an important issue in the field of machine learning and artificial intelligence applied to network contexts.This paper applies the cognitive operator to complex networks,proposes the concept of network cognition,and quantifies network characteristics through the adjacency matrix and node degrees.This paper also discusses the concept of dynamic weighted networks,analyzes the situation where the connection strength of nodes changes over time,and proposes the definition of dynamic weighted network cognition.In addition,this paper proposes an incremental computation mechanism for object-oriented,attribute-oriented,and hybrid updates to cope with scenarios such as dynamic expansion of network nodes,evolution of edge attributes,and composite updates.In dynamic weighted networks,the paper proposes a method for local updates,which efficiently handles changes of edge weights through a sliding window mechanism and a trigger-based update method,reducing the computational burden and improving efficiency.Overall,by introducing the concepts of cognitive operators and dynamic weighted networks,this paper provides a new method for analyzing and updating the influence of nodes in complex networks.

Key words: Concept-cognitive learning, Complex network, Incremental learning, Granular computing, Dynamic network