A machine learning approach for predicting hidden links in supply chain with graph neural networks

Supply chain business interruption has been identified as a key risk factor in recent years, with high-impact disruptions due to disease outbreaks, logistic issues such as the recent Suez Canal blo...

[1]  Duncan McFarlane,et al.  Extracting supply chain maps from news articles using deep neural networks , 2020, Int. J. Prod. Res..

[2]  A. Oke,et al.  Antecedents of supply chain visibility in retail supply chains: A resource-based theory perspective , 2007 .

[3]  Bilal Alsallakh,et al.  Captum: A unified and generic model interpretability library for PyTorch , 2020, ArXiv.

[4]  Jan Eric Lenssen,et al.  Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.

[5]  Alexandra Brintrup,et al.  Topological robustness of the global automotive industry , 2016, Logist. Res..

[6]  Martha C. Cooper,et al.  STRATEGIC SUPPLY CHAIN MAPPING APPROACHES , 2003 .

[7]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[8]  William L. Hamilton,et al.  Inductive Relation Prediction by Subgraph Reasoning , 2020, ICML.

[9]  M. Newman,et al.  Vertex similarity in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Rahul C. Basole,et al.  Supply Network Structure, Visibility, and Risk Diffusion: A Computational Approach , 2014, Decis. Sci..

[11]  Martin Christopher,et al.  “Supply Chain 2.0”: managing supply chains in the era of turbulence , 2011 .

[12]  Soundar R. T. Kumara,et al.  Survivability of multiagent-based supply networks: a topological perspect , 2004, IEEE Intelligent Systems.

[13]  Alexandra Brintrup,et al.  Artificial Intelligence in the Supply Chain , 2020, The Oxford Handbook of Supply Chain Management.

[14]  Edward J.S. Hearnshaw,et al.  A complex network approach to supply chain network theory , 2013 .

[15]  Alexandra Brintrup,et al.  The relationship between nested patterns and the ripple effect in complex supply networks , 2020, Int. J. Prod. Res..

[16]  Ashutosh Tiwari,et al.  Supply Networks as Complex Systems: A Network-Science-Based Characterization , 2017, IEEE Systems Journal.

[17]  Alexandre Dolgui,et al.  Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility , 2019, Handbook of Ripple Effects in the Supply Chain.

[18]  Albert-László Barabási,et al.  Network-based prediction of protein interactions , 2018, Nature Communications.

[19]  Daria Battini,et al.  Costs of resilience and disruptions in supply chain network design models: A review and future research directions , 2021 .

[20]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[21]  Thomas Y. Choi,et al.  A Theory of the Nexus Supplier: A Critical Supplier from a Network Perspective , 2014 .

[22]  Stress testing supply chains and creating viable ecosystems , 2021, Operations Management Research.

[23]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[24]  Eric T. G. Wang,et al.  The strategic value of supply chain visibility: increasing the ability to reconfigure , 2010, Eur. J. Inf. Syst..

[25]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[26]  Alexandra Brintrup,et al.  Predicting Hidden Links in Supply Networks , 2018 .

[27]  G. Antoniou,et al.  Supply chain risk management and artificial intelligence: state of the art and future research directions , 2018, Int. J. Prod. Res..

[28]  Thomas Y. Choi,et al.  Supply networks and complex adaptive systems: Control versus emergence , 2001 .

[29]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[30]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[31]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

[32]  P.S. Tan,et al.  Supply Chain Visibility: A decision making perspective , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[33]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[34]  Cristovao Silva,et al.  Improving Supply Chain Visibility With Artificial Neural Networks , 2017 .

[35]  Joan Bruna,et al.  Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.

[36]  Ankur Taly,et al.  Axiomatic Attribution for Deep Networks , 2017, ICML.

[37]  Linyuan Lü,et al.  Similarity index based on local paths for link prediction of complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[39]  Tat-Seng Chua,et al.  Neural Graph Collaborative Filtering , 2019, SIGIR.

[40]  Zhen Liu,et al.  Link prediction in complex networks: A local naïve Bayes model , 2011, ArXiv.

[41]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[42]  Yixin Chen,et al.  Weisfeiler-Lehman Neural Machine for Link Prediction , 2017, KDD.

[43]  Edoardo M. Airoldi,et al.  Mixed Membership Stochastic Blockmodels , 2007, NIPS.

[44]  Yue Wang,et al.  Predicting New Adopters via Socially-Aware Neural Graph Collaborative Filtering , 2019, CSoNet.

[45]  Alexandre Dolgui,et al.  A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 , 2020, Production Planning & Control.