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

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

By: Lizhi Xing and  Yu Han  
Open Access
|Dec 2021

Figures & Tables

Figure 1

Three possible situations in the application of XIFA algorithm. Note: (a) The source node has only one weighted edge connected to it, and 100% of its strength is allocated on it; (b) Top 20% of weighted edges carry 80% of the strength of source node. (c) Any 50% of weighted edges carry 50% of the strength of source node.
Three possible situations in the application of XIFA algorithm. Note: (a) The source node has only one weighted edge connected to it, and 100% of its strength is allocated on it; (b) Top 20% of weighted edges carry 80% of the strength of source node. (c) Any 50% of weighted edges carry 50% of the strength of source node.

Figure 2

Getting the SPFE from an original network.
Getting the SPFE from an original network.

Figure 3

Relations between ICIO table, multi-layer and single-layer ICIO network.
Relations between ICIO table, multi-layer and single-layer ICIO network.

Figure 4

Edge weight distribution of ICIO networks under the double logarithmic coordinate.
Edge weight distribution of ICIO networks under the double logarithmic coordinate.

Figure 5

MDS of GIVCN-SPFE-2014 models. Note: EG denoted by the green line is the intersection of ESP and EFE EFE, i.e., EG = EFE ∩ ESP; EB denoted by the blue line is the edge set only belonging to EFE but not ESP, i.e., EB = EFE – EG; ER denoted by the red line, contrary to EG, is the edge set only belonging to ESP but not EFE, i.e., ER = ESP – EG.
MDS of GIVCN-SPFE-2014 models. Note: EG denoted by the green line is the intersection of ESP and EFE EFE, i.e., EG = EFE ∩ ESP; EB denoted by the blue line is the edge set only belonging to EFE but not ESP, i.e., EB = EFE – EG; ER denoted by the red line, contrary to EG, is the edge set only belonging to ESP but not EFE, i.e., ER = ESP – EG.

Figure 6

Comparative result of network pruning of GIVCN models. Note: Since the pruning results of SPFE and FE method are almost the same in our models, the FE method is not shown in the figure.
Comparative result of network pruning of GIVCN models. Note: Since the pruning results of SPFE and FE method are almost the same in our models, the FE method is not shown in the figure.

Figure 7

The ratio of the average edge weight of the pruning methods and the null model.
The ratio of the average edge weight of the pruning methods and the null model.

Structural similarity of various backbones and original network_

NetworksNt% no self loopsQAP


GTDFSPFESPFEGTDFSPFESPFE
GIVCN-WIOD-SC4-201488.068100.000100.000100.000100.0001.0000.9980.9941.0001.000
GIVCN-TiVA-SC4-201491.53999.61599.61599.61599.6151.0000.9910.9961.0001.000
GIVCN-Eora-SC4-201465.079100.000100.000100.000100.0001.0001.0000.9991.0001.000
GIVCN-ADB-SC4-201477.77898.41398.81098.81098.8101.0000.9980.9961.0001.000

Illustration of properties of the FE backbone_

Database|E|dKC
WIOD3,8251.927520.81250.6231
TiVA7,4771.945127.87640.6258
Eora54,7471.950471.47620.5985
ADB6,2881.967524.32530.616

Illustration of properties of the original networks_

Networks|E|dKC
GIVCN-WIOD-SC4-201430,795 (30,976)1.006 (1.000)174.972 (176.000)1.000 (1.000)
GIVCN-TiVA-SC4-201466,600 (67,600)1.007 (1.000)257.143 (260.000)0.996 (1.000)
GIVCN-Eora-SC4-2014571,536 (571,536)1.000 (1.000)756.000 (756.000)1.000 (1.000)
GIVCN-ADB-SC4-201457,761 (63,504)1.069 (1.000)231.972 (252.000)0.971 (1.000)

Illustration of properties of the SPFE backbone_

Database|E|dKC
WIOD3,8251.927520.81250.6231
TiVA7,4771.945127.87640.6258
Eora54,7531.948571.48410.5993
ADB6,2881.967524.32530.616

Illustration of properties of the GT backbone_

Database|E|dKC
WIOD2,0095.47746.96590.5291
TiVA3,2114.35178.55980.4798
Eora2,7794.191643.85870.4397
ADB2,3614.20878.04840.4858

The procedure of network pruning based on XIFA_

ProcedureColumn Deletion of Input RelationsRow Deletion of Output Relations
NetworkW = (wij)N×N i, j ∈ [1, N]
Refactoring w1=descend(w11,w21,,wN1)Tw2=descend(w12,w22,,wN2)TwN=descend(w1N,w2N,,wNN)TW=(w1,w2,,wN)=(wsj)N×Ns,j[1,N] \matrix{ {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_1} = descend\,{{\left( {{w_{11}},{w_{21}}, \cdots ,{w_{N1}}} \right)}^T}} \cr {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_2} = descend\,{{\left( {{w_{12}},{w_{22}}, \cdots ,{w_{N2}}} \right)}^T}} \cr \cdots \cr {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_N} = descend\,{{\left( {{w_{1N}},{w_{2N}}, \cdots ,{w_{NN}}} \right)}^N}} \cr {\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over W} = \left( {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over w} }_1},{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over w} }_2}, \cdots ,{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over w} }_N}} \right) = {{\left( {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over w} }_{sj}}} \right)}_{N \times N}}} \cr {s,j \in \left[ {1,N} \right]} \cr } w1=descend(w11,w12,,w1N)w2=descend(w21,w22,,w2N)wN=descend(wN1,wN2,,wNN)W=(w1w2wN)=(wit)N×Ni,t[ 1,N ] \matrix{ {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_1} = descend\,\left( {{w_{11}},{w_{12}}, \cdots ,{w_{1N}}} \right)} \cr {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_2} = descend\,\left( {{w_{21}},{w_{22}}, \cdots ,{w_{2N}}} \right)} \cr \cdots \cr {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_N} = descend\,\left( {{w_{N1}},{w_{N2}}, \cdots ,{w_{NN}}} \right)} \cr {\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over W} \overleftarrow {} = \left( {\matrix{ {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_1}} \cr {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_2}} \cr \ldots \cr {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_N}} \cr } } \right) = {{\left( {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_{it}}} \right)}_{N \times N}}} \cr {i,t \in \left[ {1,N} \right]} \cr }
Conditions a1,a2,,aj[1,N]{s=1ajwsjs=1Nwsj1ajNs=1aj1wsjs=1Nwsj<1aj1N \matrix{ {\forall {a_1},{a_2}, \cdots ,{a_j} \in \left[ {1,N} \right]} \cr {\left\{ {\matrix{ {{{\sum\nolimits_{s = 1}^{{a_j}} {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over w} }_{sj}}} } \over {\sum\nolimits_{s = 1}^N {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over w} }_{sj}}} }} \ge 1 - {{{a_j}} \over N}} \cr {{{\sum\nolimits_{s = 1}^{{a_j} - 1} {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over w} }_{sj}}} } \over {\sum\nolimits_{s = 1}^N {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over w} }_{sj}}} }} < 1 - {{{a_j} - 1} \over N}} \cr } } \right.} \cr } b1,b2,,bj[1,N]{t=1biwitt=1Nwit1biNt=1bi1witt=1Nwit<1bi1N \matrix{ {\forall {b_1},{b_2}, \cdots ,{b_j} \in \left[ {1,N} \right]} \cr {\left\{ {\matrix{ {{{\sum\nolimits_{t = 1}^{{b_i}} {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_{it}}} } \over {\sum\nolimits_{t = 1}^N {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_{it}}} }} \ge 1 - {{{b_i}} \over N}} \cr {{{\sum\nolimits_{t = 1}^{{b_i} - 1} {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_{it}}} } \over {\sum\nolimits_{t = 1}^N {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_{it}}} }} < 1 - {{{b_i} - 1} \over N}} \cr } } \right.} \cr }
Definition XIjB=ajNXIB=(XIjB)N×1 \matrix{ {XI_j^B = {{{a_j}} \over N}} \cr {X{I^B} = {{\left( {XI_j^B} \right)}_{N \times 1}}} \cr } XIiF=biNXIF=(XIiF)N×1 \matrix{ {XI_i^F = {{{b_i}} \over N}} \cr {X{I^F} = {{\left( {XI_i^F} \right)}_{N \times 1}}} \cr }
Pruning wij={wij,wij=wsjandsaj0,otherwise {\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftarrow$}}\over w} _{ij}} = \left\{ {\matrix{ {{w_{ij}}} & , & {{w_{ij}} = {{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftharpoonup$}}\over w} }_{sj}}\,and\,s\, \le {a_j}} \cr 0 & , & {otherwise} \cr } } \right. wij={wij,wij=witandtbi0,otherwise {\vec w_{ij}} = \left\{ {\matrix{ {{w_{ij}}} & , & {{w_{ij}} = {{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}}\over w} }_{it}}\,and\,t\, \le {b_i}} \cr 0 & , & {otherwise} \cr } } \right.
Merging wij={wij,0wij0orwit0,otherwise {\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftrightarrow$}}\over w} _{ij}} = \left\{ {\matrix{ {{w_{ij}}} & {,0} & {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftarrow$}}\over w} }_{ij}} \ne 0\,or\,{{\vec w}_{it}}\, \ne } \cr 0 & , & {otherwise} \cr } } \right.
Result W=(wij)N×N \mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftrightarrow$}}\over W} = {\left( {{{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftrightarrow$}}\over w} }_{ij}}} \right)_{N \times N}}

Illustration of properties of the SP backbone_

Database|E|dKC
WIOD5334.82922.38640.4569
TiVA8064.94572.40930.3788
Eora2,4965.12582.64810.5381
ADB7524.77382.36950.4463

The basic information of ICIO databases_

DatabaseVersionTime SpanCountry / RegionIndustrial SectorAbbr
WIOD2016 Release2000–20144456WIOD2016
2013 Release1995–20114135WIOD2013
OECD-TiVA2020 Release1995–2018UnknowUnknowTiVA2020
2018 Release2005–20156536TiVA2018
2016 Release1995–20116434TiVA2016
2015 Release1995, 2000, 2005, 2008–20116234TiVA2015
Eora-MRIOV199.821990–201518926Eora26
ADB-MRIOUpdated to 20192000, 2007–20196335ADB2019

Illustration of properties of the DF backbone_

Database|E|dKC
WIOD1,3855.47746.96590.5291
TiVA2,4714.35178.55980.4798
Eora33,2894.191643.85870.4397
ADB2,2244.20878.04840.4858
DOI: https://doi.org/10.2478/jdis-2022-0002 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
Language: English
Page range: 57 - 75
Submitted on: Jul 11, 2021
Accepted on: Oct 11, 2021
Published on: Dec 11, 2021
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2021 Lizhi Xing, Yu Han, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.