2022 BOOK: AI GNN Graph Neural Networks: Foundations, Frontiers, Applications: https://. . .

Source: https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A6887046566004224001

2022 BOOK: AI GNN Graph Neural Networks: Foundations, Frontiers, Applications: https://lnkd.in/eaxxmhdq:

#Graph #Neural #Networks-#Knowledge #Graphs #Future:
#Time #Space #Complexity #QuantumUncertainty:
Where R&D-Practices Need to Advance: https://lnkd.in/esuMHr3N:

Massachusetts Institute of Technology #AI #Executive #Guide:
https://lnkd.in/eknKzm5

Princeton University Presentations: ModelRiskArbitrage.com:

SSRN: 84 Top-10 R&D Rankings, Top 1% Authors: https://lnkd.in/gFn4Vm2
R&D Impact Among Nobel Laureates.

AIMLExchange.com IN-DEPTH Know-Build-Monetize™:
#GraphNeuralNetworks https://lnkd.in/e2Bt7ycW
#KnowledgeGraphs https://lnkd.in/exzYJYqs

2022 #ArtificialIntelligence #AI #BOOK:
#Graph #Neural #Networks #Foundations #Frontiers #Applications:

Lingfei (Teddy) Wu JD.COM
Peng Cui Tsinghua University
Jian Pei Simon Fraser University
Liang Zhao Emory University

Part I Introductions

Chapter1: #Representation #Learning https://lnkd.in/eY6CtDy7 :

Chapter2: #Graph #RepresentationLearning https://lnkd.in/eRZntpkJ

Chapter3: #Graph #NeuralNetworks https://lnkd.in/eZzys7h7

Part II Foundations

Chapter4: GNNs for #Node #Classification https://lnkd.in/eagDG9qb

Chapter5: The #Expressive #Power of GNNs https://lnkd.in/egdazvdi

Chapter6: GNNs: #Scalability https://lnkd.in/eeeXEcmU

Chapter7: #Interpretability in GNNs https://lnkd.in/enaRYaJT

Chapter8: GNNs: #Adversarial #Robustness https://lnkd.in/ebdcdftJ

Part III Frontiers

Chapter9: GNNs: #Graph #Classification https://lnkd.in/e5fSUe2q

Chapter10: GNNs: #Link #Prediction
https://lnkd.in/e9GcHGQz

Chapter11: GNNs: #Graph #Generation https://lnkd.in/e95gcK8m

Chapter12: GNNs: #Graph #Transformation https://lnkd.in/eRPjBupU

Chapter13: GNNs: #Graph #Matching https://lnkd.in/eHJZYufe

Chapter14: GNNs: #Graph #Structure #Learning https://lnkd.in/eNhwRKsV

Chapter15: #Dynamic GNNs https://lnkd.in/equetDnH

Chapter16: #Heterogeneous GNNs https://lnkd.in/eBDGR_tf

Chapter17: GNNs: #AutoML https://lnkd.in/ecWY3dJk

Chapter18: GNNs: #Self #supervised #Learning https://lnkd.in/euwgpdxy

Part IV Applications

Chapter19: GNNs in Modern #Recommender #Systems https://lnkd.in/eYQTTWAt

Chapter20: GNNs in #ComputerVision https://lnkd.in/eVSuGUTe

Chapter21: GNNs in #NLP #NaturalLanguageProcessing https://lnkd.in/ezEEzx28

Chapter22: GNNs in #Program #Analysis https://lnkd.in/ebX8jdMw

Chapter23: GNNs in #Software #Mining https://lnkd.in/eEgeX-cN

Chapter24: GNN-based #Biomedical #Knowledge #Graph Mining in #Drug #Development https://lnkd.in/e4vW86Fq

Chapter25: GNNs in #Predicting #Protein #Function and #Interactions https://lnkd.in/eFmmvxg9

Chapter26: GNNs in #Anomaly #Detection https://lnkd.in/ee9XABaa

Chapter27: GNNs in #Urban #Intelligence https://lnkd.in/eCwzFbBW

We create the Digital Future™: https://lnkd.in/di8AFd5Y:
And You Can Too https://lnkd.in/esk8PEp
Global Risk Management Network LLC: Silicon Valley-Wall Street-Pentagon-Global Digital CEOs Networks:
AIMLExchange.com BRINT.com C4I-Cyber.com

2022 BOOK: AI GNN Graph Neural Networks: Foundations, Frontiers, Applications: https://lnkd.in/eaxxmhdq: #Graph #Neural #Networks-#Knowledge #Graphs #Future: #Time #Space #Complexity #QuantumUncertainty: Where R&D-Practices Need to Advance: https://lnkd.in/esuMHr3N: Massachusetts Institute of Technology #AI #Executive #Guide: https://lnkd.in/eknKzm5 Princeton University Presentations: ModelRiskArbitrage.com: SSRN: 84 Top-10 R&D Rankings, Top 1% Authors: https://lnkd.in/gFn4Vm2 R&D Impact Among Nobel Laureates. AIMLExchange.com IN-DEPTH Know-Build-Monetize™: #GraphNeuralNetworks https://lnkd.in/e2Bt7ycW #KnowledgeGraphs https://lnkd.in/exzYJYqs 2022 #ArtificialIntelligence #AI #BOOK: #Graph #Neural #Networks #Foundations #Frontiers #Applications: Lingfei (Teddy) Wu JD.COM Peng Cui Tsinghua University Jian Pei Simon Fraser University Liang Zhao Emory University Part I Introductions Chapter1: #Representation #Learning https://lnkd.in/eY6CtDy7 : Chapter2: #Graph #RepresentationLearning https://lnkd.in/eRZntpkJ Chapter3: #Graph #NeuralNetworks https://lnkd.in/eZzys7h7 Part II Foundations Chapter4: GNNs for #Node #Classification https://lnkd.in/eagDG9qb Chapter5: The #Expressive #Power of GNNs https://lnkd.in/egdazvdi Chapter6: GNNs: #Scalability https://lnkd.in/eeeXEcmU Chapter7: #Interpretability in GNNs https://lnkd.in/enaRYaJT Chapter8: GNNs: #Adversarial #Robustness https://lnkd.in/ebdcdftJ Part III Frontiers Chapter9: GNNs: #Graph #Classification https://lnkd.in/e5fSUe2q Chapter10: GNNs: #Link #Prediction https://lnkd.in/e9GcHGQz Chapter11: GNNs: #Graph #Generation https://lnkd.in/e95gcK8m Chapter12: GNNs: #Graph #Transformation https://lnkd.in/eRPjBupU Chapter13: GNNs: #Graph #Matching https://lnkd.in/eHJZYufe Chapter14: GNNs: #Graph #Structure #Learning https://lnkd.in/eNhwRKsV Chapter15: #Dynamic GNNs https://lnkd.in/equetDnH Chapter16: #Heterogeneous GNNs https://lnkd.in/eBDGR_tf Chapter17: GNNs: #AutoML https://lnkd.in/ecWY3dJk Chapter18: GNNs: #Self #supervised #Learning https://lnkd.in/euwgpdxy Part IV Applications Chapter19: GNNs in Modern #Recommender #Systems https://lnkd.in/eYQTTWAt Chapter20: GNNs in #ComputerVision https://lnkd.in/eVSuGUTe Chapter21: GNNs in #NLP #NaturalLanguageProcessing https://lnkd.in/ezEEzx28 Chapter22: GNNs in #Program #Analysis https://lnkd.in/ebX8jdMw Chapter23: GNNs in #Software #Mining https://lnkd.in/eEgeX-cN Chapter24: GNN-based #Biomedical #Knowledge #Graph Mining in #Drug #Development https://lnkd.in/e4vW86Fq Chapter25: GNNs in #Predicting #Protein #Function and #Interactions https://lnkd.in/eFmmvxg9 Chapter26: GNNs in #Anomaly #Detection https://lnkd.in/ee9XABaa Chapter27: GNNs in #Urban #Intelligence https://lnkd.in/eCwzFbBW We create the Digital Future™: https://lnkd.in/di8AFd5Y: And You Can Too https://lnkd.in/esk8PEp Global Risk Management Network LLC: Silicon Valley-Wall Street-Pentagon-Global Digital CEOs Networks: AIMLExchange.com BRINT.com C4I-Cyber.com
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Global Post AI-Quantum Finance & Trading Networks Pioneer Dr.-Eng.-Prof. Yogesh Malhotra is the “Singular Post AI-Quantum Pioneer” identified by Grok AI with R&D impact recognized among Artificial Intelligence (AI) and Quantitative Finance Nobel Laureates. As MIT-Princeton AI-ML-Cyber-Crypto-Quantum Finance & Trading and FinTech-Crypto Faculty-Industry Expert, and U.S. and Global Hedge Funds Advisory & Venture Capital CEO-CTO Teams Mentor, he has pioneered Silicon Valley-Wall Street-Pentagon Digital CEO-CTO Practices, Technologies, and Networks from world’s first-foremost-largest Global Digital Transformation Networks to New York State IDEA Award recognized Pentagon-USAF MVP Global Post AI-Quantum Networks pioneering Future of Finance and Trading practices as Trillion-Dollar Wall Street Hedge Funds and Investment Banks leader.