Parameterless Pruning Algorithms for Similarity-Weight Network and Its Application in Extracting the Backbone of Global Value Chain

Purpose:With the availability and utilization of Inter-Country Input-Output(ICIO)tables,it is possible to construct quantitative indices to assess its impact on the Global Value Chain(GVC).For the sake of visualization,ICIO networks with tremendous low-weight edges are too dense to show the substantial structure.These redundant edges,inevitably make the network data full of noise and eventually exert negative effects on Social Network Analysis(SNA).In this case,we need a method to filter such edges and obtain a sparser network with only the meaningful connections.Design/methodology/approach:In this paper,we propose two parameterless pruning algorithms from the global and local perspectives respectively,then the performance of them is examined using the ICIO table from different databases.Findings:The Searching Paths(SP)method extracts the strongest association paths from the global perspective,while Filtering Edges(FE)method captures the key links according to the local weight ratio.The results show that the FE method can basically include the SP method and become the best solution for the ICIO networks.Research limitations:There are still two limitations in this research.One is that the computational complexity may increase rapidly while processing the large-scale networks,so the proposed method should be further improved.The other is that much more empirical networks should be introduced to testify the scientificity and practicability of our methodology.Practical implications:The network pruning methods we proposed will promote the analysis of the ICIO network,in terms of community detection,link prediction,and spatial econometrics,etc.Also,they can be applied to many other complex networks with similar characteristics.Originality/value:This paper improves the existing research from two aspects,namely,considering the heterogeneity of weights and avoiding the interference of parameters.Therefore,it provides a new idea for the research of network backbone extraction.

Dynamic value iteration networks for the planning of rapidly changing UAV swarms

In an unmanned aerial vehicle ad-hoc network(UANET),sparse and rapidly mobile unmanned aerial vehicles(UAVs)/nodes can dynamically change the UANET topology.This may lead to UANET service performance issues.In this study,for planning rapidly changing UAV swarms,we propose a dynamic value iteration network(DVIN)model trained using the episodic Q-learning method with the connection information of UANETs to generate a state value spread function,which enables UAVs/nodes to adapt to novel physical locations.We then evaluate the performance of the DVIN model and compare it with the non-dominated sorting genetic algorithm II and the exhaustive method.Simulation results demonstrate that the proposed model significantly reduces the decisionmaking time for UAV/node path planning with a high average success rate.

Value of Optical Bypass in Packet Ring Networks

This paper firstly examines the value of optical bypass scheme in packet ring networks. An Integer Linear Program (ILP) formulation is presented and analytical results under different traffic patterns are given.