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Paper Citation Record · LEDGER

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures

As of 8 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2509.12484.

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pith.paper-citation-record.v1
2509.12484 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:47:39.005336Z

measured 50 of 50 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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50 of 50 outbound references displayed

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Outbound references

Observation ba0005e2-2b2a-4e39-ad0d-4668aa96a632 · outbound

This paper cites Extensions of the Deep Galerkin Method.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Extensions of the Deep Galerkin Method

Reference 1

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Observation 20f7c97d-4dff-4c7b-b949-78cfad41c981 · outbound

This paper cites Diffusion-convolutional neural networks.Advances in neural information processing systems, 29, 2016.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Diffusion-convolutional neural networks.Advances in neural information processing systems, 29, 2016

Reference 2

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Observation 4d18c27f-342d-40c1-8b0c-e9eb8a8c0dc5 · outbound

This paper cites Deep neural networks al- gorithms for stochastic control problems on finite horizon: numerical applications.Methodology and Computing in Applied Probability, 24(1):143–178, 2022.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Deep neural networks al- gorithms for stochastic control problems on finite horizon: numerical applications.Methodology and Computing in Applied Probability, 24(1):143–178, 2022

Reference 3

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Observation 4a3d13fc-a5af-433b-8943-2fefd07a8e80 · outbound

This paper cites Netgan: Generating graphs via random walks.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Netgan: Generating graphs via random walks

Reference 4

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Observation 96863636-d6de-4269-b584-e5bf6333b598 · outbound

This paper cites Translating embeddings for modeling multi-relational data.Advances in neu- ral information processing systems, 26, 2013.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Translating embeddings for modeling multi-relational data.Advances in neu- ral information processing systems, 26, 2013

Reference 5

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Observation b35f979a-eabf-4797-bee7-0eee260381c8 · outbound

This paper cites Some notes on computation of games solutions.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Some notes on computation of games solutions

Reference 6

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Observation 50c2f9b6-5b26-4f7d-9040-99f07bd56f54 · outbound

This paper cites Iterative solution of games by fictitious play.Activity Analysis of Production and Allocation, 13(1):374–376, 1951.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Iterative solution of games by fictitious play.Activity Analysis of Production and Allocation, 13(1):374–376, 1951

Reference 7

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Observation 22b7f783-1031-40d4-b102-4fc2a39e1c07 · outbound

This paper cites Princeton University Press, 2009.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Princeton University Press, 2009

Reference 8

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Observation 932c1c7d-6585-4af3-82db-f49874357c1e · outbound

This paper cites Springer, 2018.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Springer, 2018

Reference 9

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Observation c0e13c41-0640-4be8-82fb-26dca1f70818 · outbound

This paper cites Mean field games and systemic risk.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Mean field games and systemic risk

Reference 10

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Observation 91530e2b-f44c-429a-99f5-04c6e1a98330 · outbound

This paper cites American Mathematical Soc., 1997.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures American Mathematical Soc., 1997

Reference 11

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Observation 1f645d90-7e08-44f5-928a-b9b7358df68e · outbound

This paper cites Approximation by superpositions of a sigmoidal function.Mathematics of control, signals and systems, 2(4):303–314, 1989.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Approximation by superpositions of a sigmoidal function.Mathematics of control, signals and systems, 2(4):303–314, 1989

Reference 12

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Observation 520db3a1-5e88-409f-bf11-e02b92bb7ecb · outbound

This paper cites The complexity of computing a nash equilibrium.Communications of the ACM, 52(2):89–97, 2009.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures The complexity of computing a nash equilibrium.Communications of the ACM, 52(2):89–97, 2009

Reference 13

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Observation eb9c1481-b6c9-40d0-9c89-453cd0fa3604 · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.Advances in neural information processing systems, 29, 2016.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Convolutional neural networks on graphs with fast localized spectral filtering.Advances in neural information processing systems, 29, 2016

Reference 14

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Observation 68c18e68-5446-44da-ad3b-aa185ac2bf39 · outbound

This paper cites Learning from one graph: transductive learning guarantees via the geometry of small random worlds.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Learning from one graph: transductive learning guarantees via the geometry of small random worlds

Reference 15

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Observation a1a53eef-0d0b-4a97-b339-1429a9e0f614 · outbound

This paper cites Convolutional networks on graphs for learning molecular fingerprints.Advances in neural information processing systems, 28, 2015.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Convolutional networks on graphs for learning molecular fingerprints.Advances in neural information processing systems, 28, 2015

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Observation 65db79d4-8ad3-4036-9558-64ab3b80325c · outbound

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Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Unresolved cited work

Reference 17

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Observation 7cad87a6-7e5f-42d5-accf-e61dbe0f4965 · outbound

This paper cites Financial networks and contagion.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Financial networks and contagion

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Observation 03b3af27-1264-415b-abb0-3c708a98bef4 · outbound

This paper cites Protein interface prediction using graph convolutional networks.Advances in neural information processing systems, 30, 2017.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Protein interface prediction using graph convolutional networks.Advances in neural information processing systems, 30, 2017

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Observation db6c8def-0e81-477f-bcb7-36bf86b8c8fc · outbound

This paper cites The development of social network analysis.A Study in the Sociology of Science, 1(687):159–167, 2004.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures The development of social network analysis.A Study in the Sociology of Science, 1(687):159–167, 2004

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Observation 5bb7b7d4-6eb3-4453-830c-3e699199f2ad · outbound

This paper cites Consensus-based optimal operation of multi-agent renewable energy hubs considering various graph topologies.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Consensus-based optimal operation of multi-agent renewable energy hubs considering various graph topologies

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Observation 73f1e561-eeb8-4a93-a1dc-e842aecb101c · outbound

This paper cites Deep learning approximation for stochastic control problems.Deep Reinforcement Learning Workshop, NIPS, 2016.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Deep learning approximation for stochastic control problems.Deep Reinforcement Learning Workshop, NIPS, 2016

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Observation d3214407-28a4-4011-b729-91a0b6a6c501 · outbound

This paper cites Deep fictitious play for finding Markovian Nash equilibrium in multi-agent games.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Deep fictitious play for finding Markovian Nash equilibrium in multi-agent games

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Observation d3afe6e0-c1f4-4176-8cd5-2c4c81f9466d · outbound

This paper cites Convergence of deep fictitious play for stochastic differential games.Frontiers of Mathematical Finance, 1(2), 2022.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Convergence of deep fictitious play for stochastic differential games.Frontiers of Mathematical Finance, 1(2), 2022

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Observation 0876cda4-d12c-465d-b38d-dd7e33014682 · outbound

This paper cites A brief review of the Deep BSDE method for solving high-dimensional partial differential equations.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures A brief review of the Deep BSDE method for solving high-dimensional partial differential equations

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Observation 842d15ae-0ddd-4a37-91ed-57a811766775 · outbound

This paper cites Deep residual learning for image recognition.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Deep residual learning for image recognition

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Observation 381a3029-3562-4839-928b-76ccaad3f3c2 · outbound

This paper cites Universal approximation of an un- known mapping and its derivatives using multilayer feedforward networks.Neural networks, 3(5):551–560, 1990.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Universal approximation of an un- known mapping and its derivatives using multilayer feedforward networks.Neural networks, 3(5):551–560, 1990

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Observation 131e2d41-a164-42a9-bd78-7ccae817f4f6 · outbound

This paper cites Deep fictitious play for stochastic differential games.Communications in Math- ematical Sciences, 19(2):325–353, 2021.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Deep fictitious play for stochastic differential games.Communications in Math- ematical Sciences, 19(2):325–353, 2021

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Observation d13903a6-a2a0-4b03-8174-42742356e289 · outbound

This paper cites Recent Developments in Machine Learning Methods for Stochastic Control and Games.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Recent Developments in Machine Learning Methods for Stochastic Control and Games

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Observation 4f175500-bc87-4f4d-b40b-efed0cddc58f · outbound

This paper cites Finite-agent stochastic differential games on large graphs: I.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Finite-agent stochastic differential games on large graphs: I

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Observation 9449ce4b-67ed-422d-b591-6e92d081765a · outbound

This paper cites A short tutorial on the Weisfeiler-Lehman test and its variants.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures A short tutorial on the Weisfeiler-Lehman test and its variants

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Observation e66757cb-2d6c-4ce1-9af0-5d93ab5b778f · outbound

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Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Deep backward schemes for high-dimensional nonlinear PDEs.Mathematics of Computation, 89(324):1547–1579, 2020

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Observation 7ce514b7-aa45-4033-a76a-57cb0e30ac0d · outbound

This paper cites Systemic risk in financial networks: A survey.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Systemic risk in financial networks: A survey

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Observation 7c560ddb-7578-49b9-9096-89f4ba6d59eb · outbound

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Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Distributed robotic sensor networks: An information-theoretic approach.The International Journal of Robotics Research, 31(10):1134–1154, 2012

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Observation 84c6c028-cd45-490c-9c32-5f9a2432bd38 · outbound

This paper cites Kipf and Max Welling.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Kipf and Max Welling

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Observation 72ceae0d-9102-4f6e-b8cd-ac178f357461 · outbound

This paper cites Multilayer feedforward networks with a nonpolynomial activation function can approximate any function.Neural networks, 6(6):861–867, 1993.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Multilayer feedforward networks with a nonpolynomial activation function can approximate any function.Neural networks, 6(6):861–867, 1993

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source=pdf_text observed=2026-08-04T16:47:38.966284Z digest=sha256:4182fe17aaf3a37c805f660efea234656eb2a147713b367fb510f693fd72fb7a

Observation 676119a2-d90d-4a96-a326-1622a83b1816 · outbound

This paper cites A survey of sparse-learning methods for deep neural networks.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures A survey of sparse-learning methods for deep neural networks

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source=pdf_text observed=2026-08-04T16:47:38.969128Z digest=sha256:cf3c8d2ec62cac4371894e53f70f740e40798ce78ee8a82b44d0bc97bf5ef411

Observation d4d0ebfd-6935-4044-8cf9-9388a51ac847 · outbound

This paper cites A comparison of three methods for selecting values of input variables in the analysis of output from a computer code.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures A comparison of three methods for selecting values of input variables in the analysis of output from a computer code

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source=pdf_text observed=2026-08-04T16:47:38.971838Z digest=sha256:c0d05c108d2a00c952fc00dd2283270a259a71caeae0cd2627eba3d437361a8e

Observation 7165d0d0-f3d5-4ca3-8ab8-d742f808e233 · outbound

This paper cites SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks

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source=pdf_text observed=2026-08-04T16:47:38.974331Z digest=sha256:d134a2bba5d8b197e559e58a59e77d8eeec02090a9c9595dd222a36ab2c8abcc

Observation 1f671337-680d-43e5-ad4f-503bcb7a49d7 · outbound

This paper cites Backward stochastic differential equations and quasilin- ear parabolic partial differential equations.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Backward stochastic differential equations and quasilin- ear parabolic partial differential equations

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source=pdf_text observed=2026-08-04T16:47:38.977629Z digest=sha256:f7a971d61a1f76df34dacf46e8a7bccada3fd8366ca47a6eba152c066492354d

Observation ff9c3158-b492-4a77-a703-70772ba90578 · outbound

This paper cites Forward-backward stochastic differential equations and quasilinear parabolic pdes.Probability theory and related fields, 114:123–150, 1999.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Forward-backward stochastic differential equations and quasilinear parabolic pdes.Probability theory and related fields, 114:123–150, 1999

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source=pdf_text observed=2026-08-04T16:47:38.980293Z digest=sha256:db3516f4e9fb69cb7463f93a5a0d3a72f35b4a5f26ef10b32431638f59d3ceb8

Observation acfe3b9c-16a5-4594-be04-f81b27bf4372 · outbound

This paper cites an unresolved cited work.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Unresolved cited work

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source=pdf_text observed=2026-08-04T16:47:38.982682Z digest=sha256:86f23e667fe6faf0fdb3e8263ce5d386fdb2d186452db74cf900d5b6978e5094

Observation 49deedd5-7567-447f-af0e-943cf5e89a3c · outbound

This paper cites Dgm: A deep learning algorithm for solving partial differential equations.Journal of computational physics, 375:1339–1364, 2018.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Dgm: A deep learning algorithm for solving partial differential equations.Journal of computational physics, 375:1339–1364, 2018

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source=pdf_text observed=2026-08-04T16:47:38.985232Z digest=sha256:f6b8cf48512cef42c0bef300653e32ef2358c68b11c7ef0d779789d4a9c03c5a

Observation 5323024c-27a5-4db3-9bf0-2b55cff2fa35 · outbound

This paper cites Social network anal- ysis: An overview.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5):e1256, 2018.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Social network anal- ysis: An overview.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5):e1256, 2018

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source=pdf_text observed=2026-08-04T16:47:38.988355Z digest=sha256:3e19f4d8ea3687ad038bf8e65a2a18561d49034b2dc70bf491c3474c8a62337e

Observation 92122f18-22b8-4815-9633-e572d6837300 · outbound

This paper cites Networks, dynamics, and the small-world phenomenon.American Journal of sociology, 105(2):493–527, 1999.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Networks, dynamics, and the small-world phenomenon.American Journal of sociology, 105(2):493–527, 1999

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source=pdf_text observed=2026-08-04T16:47:38.991006Z digest=sha256:954a5388dee7d9c99fbc67ddd6ee0a830fa3c018109c037914a6d143b92c1c5f

Observation 87b54be3-8c4e-41fc-a2db-66590ef10882 · outbound

This paper cites A comprehensive survey on graph neural networks.IEEE transactions on neural networks and learning systems, 32(1):4–24, 2020.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures A comprehensive survey on graph neural networks.IEEE transactions on neural networks and learning systems, 32(1):4–24, 2020

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source=pdf_text observed=2026-08-04T16:47:38.993544Z digest=sha256:e1ebdc2b9991815349398e462ba38387613606ed364b06ec0cd4359d2f42d311

Observation 9bc13b13-e58f-438a-b26c-0c5f9ab04484 · outbound

This paper cites How powerful are graph neural networks? InInternational Conference on Learning Representations (ICLR), 2019.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures How powerful are graph neural networks? InInternational Conference on Learning Representations (ICLR), 2019

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source=pdf_text observed=2026-08-04T16:47:38.996654Z digest=sha256:1645f4c5eacbe8ac2eaec05624d77f04602d5c514952bb3ed3d49f75cb82513a

Observation d7fb1491-a6bd-435d-b065-6265e0e4c6b3 · outbound

This paper cites Graph convolutional networks: a comprehensive review.Computational Social Networks, 6(1):1–23, 2019.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Graph convolutional networks: a comprehensive review.Computational Social Networks, 6(1):1–23, 2019

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source=pdf_text observed=2026-08-04T16:47:39.000119Z digest=sha256:485fde1af49c39acc987858eac9600e4feec9147e54c9537335e7eca99525ea4

Observation a158bbe7-5a5d-40ba-b3a8-825fdfc29560 · outbound

This paper cites an unresolved cited work.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures Unresolved cited work

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source=pdf_text observed=2026-08-04T16:47:39.002641Z digest=sha256:cf28154b77f86181d746d36e74c02c873309e629983b4aca88250e5f9c0064bb

Observation 5a27c5d5-7cf2-4ec2-a7b1-ea920add982a · outbound

This paper cites crIdin 0 γ(x) pr,p 0 # , W (k) pr,q =.

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures crIdin 0 γ(x) pr,p 0 # , W (k) pr,q =

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source=pdf_text observed=2026-08-04T16:47:39.005336Z digest=sha256:468f3803061515a72e14567f2c7e4a8bda3f8dde7b38f93a27b3b19af3fdb54e

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