Temporal link prediction on dynamic graphs based on spatial-temporal normalizing flow
Dynamic graphs,as a crucial branch of graph theory,possess comprehensive capabilities for capturing the dynamic changes in relationships between nodes.Modeling real-world relational networks using dynamic graphs and dynamically predicting the link relationships between nodes in the future have become current research hotspots.However,due to the phenomenon of weak relationships,dynamic link prediction in weighted networks faces significant challenges.Addressing this issue,this paper proposes a method based on regularization flow called DynWFlow(dynamic weight flow),which is based on normalizing flow(NF).This method,starting from a generative perspective,adaptively evaluates the importance of link information between nodes,enabling precise extraction of link features and effectively resolving the dynamic link prediction problem.Particularly,for situations involving weak relationships,it is proposed to assess the importance of different linkages by utilizing the similarity of weights in the neighbor node set,achieving further capture of implicit relationships between nodes.Experimental results with extensive real-world data from multiple domains indicate that the performance of the proposed dynamic link prediction method,DynWFlow,based on NF,is significantly superior to other prediction algorithms.
dynamic link predictionnormalizing flowdynamic graphsspatial-temporal representationgraph embedding
NETL
NSTL
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