TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows | Paper Citation Record

Observation 653a8993-fa70-4e25-bfc8-9be6ff794df0 · outbound

This paper cites Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing

Reference 1

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Observation 837b765f-d242-4875-9aea-ccab4874a444 · outbound

This paper cites On the bottleneck of graph neural networks and its practical implications.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows On the bottleneck of graph neural networks and its practical implications

Reference 2

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This paper cites On vanishing gradients, over- smoothing, and over-squashing in gnns: Bridging recurrent and graph learning.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows On vanishing gradients, over- smoothing, and over-squashing in gnns: Bridging recurrent and graph learning

Reference 3

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Observation 63222f8e-98d0-4cb8-83a5-10d6078ed39c · outbound

This paper cites Accurate prediction of protein structures and interactions using a three-track neural network.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Accurate prediction of protein structures and interactions using a three-track neural network

Reference 4

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Observation f0575e5a-102f-4c2e-ad79-1e5e1741e572 · outbound

This paper cites Directional graph networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Directional graph networks

Reference 5

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Observation b1160578-65bc-4b7b-8698-15c37612d9ba · outbound

This paper cites Graph Mamba: Towards Learning on Graphs with State Space Models.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Graph Mamba: Towards Learning on Graphs with State Space Models

Reference 6

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Observation b5adc7cb-3442-41f2-9097-6ad7ffd3dac1 · outbound

This paper cites Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network

Reference 7

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Observation 534d332e-23a3-40eb-9b8a-507fbefa889a · outbound

This paper cites Understanding oversquashing in gnns through the lens of effective resistance.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Understanding oversquashing in gnns through the lens of effective resistance

Reference 8

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Observation 362e2c61-1e56-412f-9f7a-2199c2b63824 · outbound

This paper cites Beyond low-frequency information in graph convolutional networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Beyond low-frequency information in graph convolutional networks

Reference 9

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This paper cites Convex optimization.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Convex optimization

Reference 10

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This paper cites Worrall, and Max Welling.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Worrall, and Max Welling

Reference 11

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Observation f4b577b2-6d0d-474e-a981-f5a929137ae8 · outbound

This paper cites Residual Gated Graph ConvNets.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Residual Gated Graph ConvNets

Reference 12

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This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 13

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Observation 40db5b14-6da4-4aee-b708-3ace3a9ae556 · outbound

This paper cites A Note on Over-Smoothing for Graph Neural Networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows A Note on Over-Smoothing for Graph Neural Networks

Reference 14

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Observation 328a9964-a9aa-4d59-b09f-3a6b1bbc4618 · outbound

This paper cites Beltrami flow and neural diffusion on graphs.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Beltrami flow and neural diffusion on graphs

Reference 15

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Observation d75ab464-06fb-428e-b54d-36e315e8a25d · outbound

This paper cites GRAND: Graph neural diffusion.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows GRAND: Graph neural diffusion

Reference 16

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Observation 03d01af8-95fd-409f-aaad-ceab762d9edb · outbound

This paper cites Simple and Deep Graph Convolutional Networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Simple and Deep Graph Convolutional Networks

Reference 17

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This paper cites Neural ordinary differential equations.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Neural ordinary differential equations

Reference 18

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This paper cites Adaptive universal generalized pagerank graph neural network.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Adaptive universal generalized pagerank graph neural network

Reference 19

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Observation ae09191b-60b2-406a-82d3-db71e7e5503a · outbound

This paper cites Gread: Graph neural reaction-diffusion networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Gread: Graph neural reaction-diffusion networks

Reference 20

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Observation 42b2b07b-d113-47a3-852c-0043d8d4c5cd · outbound

This paper cites Multi-channel Deep 3D Face Recognition.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Multi-channel Deep 3D Face Recognition

Reference 21

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Observation 70e3b6d1-4f0b-4274-acd2-96a7fa371a80 · outbound

This paper cites From block-toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked transformers.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows From block-toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked transformers

Reference 22

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Observation ffd18064-af35-4d6f-97e1-8f660b94ff37 · outbound

This paper cites Principal Neighbourhood Aggregation for Graph Nets.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Principal Neighbourhood Aggregation for Graph Nets

Reference 23

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This paper cites On over-squashing in message passing neural networks: the impact of width, depth, and topology.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows On over-squashing in message passing neural networks: the impact of width, depth, and topology

Reference 24

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Observation 30ef06ba-f79f-4ca9-9d40-6a58fd146348 · outbound

This paper cites Understanding convolution on graphs via energies.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Understanding convolution on graphs via energies

Reference 25

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Observation 86c0422c-8e43-4944-951a-9cfba1904ada · outbound

This paper cites Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily

Reference 26

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Observation 7e9261f7-74b2-4d7c-8d83-6ecf2c732a51 · outbound

This paper cites Implicit generation and modeling with energy based models.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Implicit generation and modeling with energy based models

Reference 27

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This paper cites A Generalization of Transformer Networks to Graphs.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows A Generalization of Transformer Networks to Graphs

Reference 28

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Observation fcc79015-0ba2-40c0-b9d4-56d67195ba50 · outbound

This paper cites Graph neural networks with learnable structural and positional representations.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Graph neural networks with learnable structural and positional representations

Reference 29

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This paper cites Long Range Graph Benchmark.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Long Range Graph Benchmark

Reference 30

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This paper cites Benchmarking graph neural networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Benchmarking graph neural networks

Reference 31

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This paper cites PDE-GCN: Novel architectures for graph neural networks motivated by partial differential equations.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows PDE-GCN: Novel architectures for graph neural networks motivated by partial differential equations

Reference 32

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This paper cites Feature transportation improves graph neural networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Feature transportation improves graph neural networks

Reference 33

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Observation 8520524c-246c-42ad-9fcb-76eab1aec656 · outbound

This paper cites On the temporal domain of differential equation inspired graph neural networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows On the temporal domain of differential equation inspired graph neural networks

Reference 34

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This paper cites Lyapunov-Based Graph Neural Networks for Adaptive Control of Multi-Agent Systems.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Lyapunov-Based Graph Neural Networks for Adaptive Control of Multi-Agent Systems

Reference 35

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This paper cites Bronstein, and Ismail Ilkan Ceylan.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Bronstein, and Ismail Ilkan Ceylan

Reference 36

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Observation e6d5e96e-afa9-4b36-a5a5-0b6b00193123 · outbound

This paper cites A large-scale database for graph representation learning.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows A large-scale database for graph representation learning

Reference 37

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Observation 9aa0ff67-6ec5-4ff1-a13f-1cb999e37ac8 · outbound

This paper cites Physics-informed graph neural galerkin networks: A unified framework for solving pde-governed forward and inverse problems.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Physics-informed graph neural galerkin networks: A unified framework for solving pde-governed forward and inverse problems

Reference 38

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Observation c9074d12-57c9-41bb-85c9-84b8e1f62fd2 · outbound

This paper cites Diffusion Improves Graph Learning.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Diffusion Improves Graph Learning

Reference 39

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This paper cites On the trade-off between over-smoothing and over-squashing in deep graph neural networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows On the trade-off between over-smoothing and over-squashing in deep graph neural networks

Reference 40

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Observation fce9e59f-03ed-4c98-a345-622ba51b7fda · outbound

This paper cites Anti-Symmetric DGN: a stable architecture for Deep Graph Networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Anti-Symmetric DGN: a stable architecture for Deep Graph Networks

Reference 41

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Observation d67ed53f-a586-4978-a8d4-59996ad39a3e · outbound

This paper cites On oversquashing in graph neural networks through the lens of dynamical systems.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows On oversquashing in graph neural networks through the lens of dynamical systems

Reference 42

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This paper cites Efficiently modeling long sequences with structured state spaces.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Efficiently modeling long sequences with structured state spaces

Reference 43

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Observation ea980318-18a0-48ea-afcb-bbb41597db4b · outbound

This paper cites Egc: Image generation and classification via a diffusion energy-based model.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Egc: Image generation and classification via a diffusion energy-based model

Reference 44

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Observation 5ef1aeaf-fb01-4178-82db-196f582deddc · outbound

This paper cites Drew: Dynamically rewired message passing with delay.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Drew: Dynamically rewired message passing with delay

Reference 45

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Observation fa5eb6e6-0c40-497a-8411-b6437ab30964 · outbound

This paper cites Hamilton, Rex Ying, and Jure Leskovec.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Hamilton, Rex Ying, and Jure Leskovec

Reference 46

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Observation 9dfdd0f0-8b49-4cec-8388-19c77a11e714 · outbound

This paper cites From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond

Reference 47

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Observation 3741fd4c-eb0d-4463-9782-420f8564919b · outbound

This paper cites Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks

Reference 48

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Observation 6bd25e75-b0b4-4ff9-b844-12b8fefdb313 · outbound

This paper cites Long short-term memory.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Long short-term memory

Reference 49

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Observation 72d95d9d-3cf5-4126-94a2-a4a411b51dd3 · outbound

This paper cites Strategies for Pre-training Graph Neural Networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Strategies for Pre-training Graph Neural Networks

Reference 50

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Observation e7311c36-c4c9-4607-9efb-ce20fe970d3f · outbound

This paper cites Global self- attention as a replacement for graph convolution.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Global self- attention as a replacement for graph convolution

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.469268Z

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Observation 83e00248-561f-44e2-9821-d299a269e93e · outbound

This paper cites Spectral graph pruning against over-squashing and over-smoothing.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Spectral graph pruning against over-squashing and over-smoothing

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.458296Z

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Observation 08a9c6d6-7f44-46eb-a456-4e80128e36d6 · outbound

This paper cites Nonlinear systems, volume 3.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Nonlinear systems, volume 3

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.445282Z

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This paper cites Kipf and M.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Kipf and M

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.433600Z

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Observation dc63a956-fb37-4070-be0f-8f9169e23c3e · outbound

This paper cites Bayan Bruss, and Tom Goldstein.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Bayan Bruss, and Tom Goldstein

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.420274Z

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Observation f64220b8-2d25-4c84-a8d9-199472f0b91f · outbound

This paper cites Rethinking graph transformers with spectral attention.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Rethinking graph transformers with spectral attention

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.408555Z

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Observation e8be2647-8e48-468c-ae53-1a79d7024453 · outbound

This paper cites an unresolved cited work.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-05T23:39:48.397019Z

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Observation 47ac86af-c2fa-4193-bb05-4acc71985145 · outbound

This paper cites A tutorial on energy-based learning.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows A tutorial on energy-based learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.384921Z

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Observation fae87206-8625-4bfe-825e-286cc2eee42a · outbound

This paper cites Finding global homophily in graph neural networks when meeting heterophily.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Finding global homophily in graph neural networks when meeting heterophily

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.373536Z

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Observation 3c49000c-535b-4e65-920d-55adb381a198 · outbound

This paper cites Toloker Graph: Interaction of Crowd Annotators, February 2023.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Toloker Graph: Interaction of Crowd Annotators, February 2023

Reference 61

Resolution: verified exact; doi, observed 2026-08-05T23:39:46.798712Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8cfa8343-5d56-48b3-8322-f502ce62e81b · outbound

This paper cites GraphEBM: Molecular graph generation with energy-based models.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows GraphEBM: Molecular graph generation with energy-based models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.360299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4e104999-b88c-4a3e-8255-51f4b46f9bce · outbound

This paper cites The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.252436Z

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Unavailable: canonical work link unavailable.

Observation 778cc62b-9381-44e8-9cc4-d2581060aeef · outbound

This paper cites Graph inductive biases in transformers without message passing.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Graph inductive biases in transformers without message passing

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.349248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0566e39f-c3f2-4c0e-8e50-9bc44a6091ee · outbound

This paper cites Qdc: Quantum diffusion convolution kernels on graphs, 2023.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Qdc: Quantum diffusion convolution kernels on graphs, 2023

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.337436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c01912c-f197-44f1-b5d8-730fce036ba1 · outbound

This paper cites A fractional graph laplacian approach to oversmoothing.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows A fractional graph laplacian approach to oversmoothing

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.324510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e463088a-393e-46a0-9986-ecf672726132 · outbound

This paper cites Simplifying approach to node classification in graph neural networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Simplifying approach to node classification in graph neural networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.312414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 941d10e1-b759-409a-8beb-bf20eff4e2eb · outbound

This paper cites Attending to graph transformers.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Attending to graph transformers

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.301760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 225d4764-eee2-4a8b-bdcb-3b4b414dfba9 · outbound

This paper cites Nocedal and S.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Nocedal and S

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.290318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 52a5a9e2-118f-44b8-8733-5070cf613a43 · outbound

This paper cites Revisiting Graph Neural Networks: All We Have is Low-Pass Filters.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.281000Z

Source-reported events for the cited work

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Observation d9ffe4b8-94fe-4d55-b936-1b01ae85df22 · outbound

This paper cites Graph neural networks exponentially lose expressive power for node classification.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Graph neural networks exponentially lose expressive power for node classification

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.279860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 99583dd7-8813-4b56-96a4-caeb11bd3d92 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Pytorch: An imperative style, high-performance deep learning library

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.290834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14a3c9f6-8efb-4788-9dbc-e78fddf11176 · outbound

This paper cites Beyond over-smoothing: Uncovering the trainability challenges in deep graph neural networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Beyond over-smoothing: Uncovering the trainability challenges in deep graph neural networks

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.258355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 267f0df6-7af7-44c3-a8f4-efd87dd1c2b3 · outbound

This paper cites A critical look at the evaluation of GNNs under heterophily: Are we re- ally making progress? In The Eleventh International Conference on Learning Representations,.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows A critical look at the evaluation of GNNs under heterophily: Are we re- ally making progress? In The Eleventh International Conference on Learning Representations,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.245457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e7967d9c-7540-4455-b4eb-0372a664a8f7 · outbound

This paper cites Recipe for a General, Powerful, Scalable Graph Transformer.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Recipe for a General, Powerful, Scalable Graph Transformer

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.317375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9204b7c0-5ff8-4518-be99-b5fc9e4ba8ae · outbound

This paper cites Recipe for a General, Powerful, Scalable Graph Transformer.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Recipe for a General, Powerful, Scalable Graph Transformer

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.322170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0330d85b-9fd7-4fb3-92ce-ef631f0c84ea · outbound

This paper cites Graph neural networks for materials science and chemistry.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Graph neural networks for materials science and chemistry

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.325492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 515afaf9-856c-4ea6-bc71-4671393a502c · outbound

This paper cites Lyanet: A lyapunov framework for training neural odes.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Lyanet: A lyapunov framework for training neural odes

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.209987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a7d6221-e243-4925-a9bf-ebbf0600f4c1 · outbound

This paper cites Graph-coupled oscillator networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Graph-coupled oscillator networks

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.198763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f658564b-6ec8-45fd-b573-8d10aa6d4e4e · outbound

This paper cites A Survey on Oversmoothing in Graph Neural Networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows A Survey on Oversmoothing in Graph Neural Networks

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.336055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bfdc4af-9e80-4750-83ac-87df6bb46aac · outbound

This paper cites Deep neural networks motivated by partial differential equations.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Deep neural networks motivated by partial differential equations

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.187629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 806d9dce-ed76-4a22-a66b-a80213e2140f · outbound

This paper cites Qm/mm methods for biomolecular systems.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Qm/mm methods for biomolecular systems

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.175859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a20d2ff8-c089-4101-a640-98cd97761036 · outbound

This paper cites Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.347371Z

Source-reported events for the cited work

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Observation 55f3551a-7ba6-4232-b657-4fab5783a4ff · outbound

This paper cites Masked label prediction: Unified message passing model for semi-supervised classifica- tion.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Masked label prediction: Unified message passing model for semi-supervised classifica- tion

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.351018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ea2dfb0-0b63-4b96-95b2-f9a3b3de19e8 · outbound

This paper cites Rahmani, and Marzieh Aghaei.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Rahmani, and Marzieh Aghaei

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.166492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d5ec6284-acdd-489a-9c6e-4e78eb03cdba · outbound

This paper cites Where did the gap go? reassessing the long-range graph benchmark.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Where did the gap go? reassessing the long-range graph benchmark

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.155899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation adef06a8-4848-497c-8532-a8e0acbca58e · outbound

This paper cites Walking out of the weis- feiler leman hierarchy: Graph learning beyond message passing.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Walking out of the weis- feiler leman hierarchy: Graph learning beyond message passing

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.134332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 10f10dd2-6684-46b6-857e-fbd8628dcd9a · outbound

This paper cites Bronstein.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Bronstein

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.372579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 844f643b-af78-4e86-81da-723900977566 · outbound

This paper cites Capturing graphs with hypo-elliptic diffusions.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Capturing graphs with hypo-elliptic diffusions

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.115138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e2e5f252-51b2-494a-823e-7fbea0689841 · outbound

This paper cites Vaswani et al.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Vaswani et al

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.101969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b3e89835-42ba-4f7c-ab00-268256b6c29f · outbound

This paper cites an unresolved cited work.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-05T23:39:48.144423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23c5b6e2-2445-43a7-9fa7-f015dde1ead7 · outbound

This paper cites Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-05T23:39:46.394973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a4d4f77-5809-4e86-a3e5-98488b4aede5 · outbound

This paper cites The heterophilic snowflake hypothesis: Training and empowering gnns for heterophilic graphs.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows The heterophilic snowflake hypothesis: Training and empowering gnns for heterophilic graphs

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.081202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2c62ac03-e3fc-4d8f-963b-9578b9c9b8c4 · outbound

This paper cites How powerful are spectral graph neural networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows How powerful are spectral graph neural networks

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.070486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8a2a89d0-d098-45ad-9da2-cc6dfc74b832 · outbound

This paper cites Dissecting the Diffusion Process in Linear Graph Convolutional Networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Dissecting the Diffusion Process in Linear Graph Convolutional Networks

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.059979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70f7f8df-2977-46c3-877e-7fc4f3f66257 · outbound

This paper cites Graph attention networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Graph attention networks

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.091485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 90671510-482e-4ff1-9181-5ee050424836 · outbound

This paper cites Recent successes of the energy landscape theory of protein folding and function.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Recent successes of the energy landscape theory of protein folding and function

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.034701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 466b52af-1f8e-444e-9dd2-1fafff29a4fa · outbound

This paper cites Continuous graph neural networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Continuous graph neural networks

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.022315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4f600fce-b3ad-4cd6-9fc3-5d05c8f11724 · outbound

This paper cites A theory of generative convnet.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows A theory of generative convnet

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:47.996803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 30747cab-0b31-4a43-a0b7-77c0a1672d47 · outbound

This paper cites How powerful are graph neural networks? In International Conference on Learning Representations , 2019.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows How powerful are graph neural networks? In International Conference on Learning Representations , 2019

Reference 103

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:47.984651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 046a9840-0409-4d30-84d9-6ef3dc02c999 · outbound

This paper cites ACMP: Allen-cahn message passing with attractive and repulsive forces for graph neural networks.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows ACMP: Allen-cahn message passing with attractive and repulsive forces for graph neural networks

Reference 104

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:39:48.047134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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