GRAMA: Adaptive Graph Autoregressive Moving Average Models | Paper Citation Record

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This paper cites write newline.

GRAMA: Adaptive Graph Autoregressive Moving Average Models write newline

Reference 1

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Observation d9c805ae-134f-466f-bbc2-0086881b67b6 · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing

Reference 2

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Observation bbe6d8ad-1737-448d-8aff-fb93fd3ae2ea · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models On the bottleneck of graph neural networks and its practical implications

Reference 3

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Observation 1139cc3c-9af1-40e6-bb9b-6cb67ef01ff8 · outbound

This paper cites State Space Models: A Unifying Framework.

GRAMA: Adaptive Graph Autoregressive Moving Average Models State Space Models: A Unifying Framework

Reference 4

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This paper cites Unitary evolution recurrent neural networks.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Unitary evolution recurrent neural networks

Reference 5

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Accurate prediction of protein structures and interactions using a three-track neural network

Reference 6

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This paper cites A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting.

GRAMA: Adaptive Graph Autoregressive Moving Average Models A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

Reference 7

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Observation 1b48645e-49ed-4046-89f6-2a06dce5b447 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

GRAMA: Adaptive Graph Autoregressive Moving Average Models xLSTM: Extended Long Short-Term Memory

Reference 8

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Observation 3172955f-6e79-431c-bc0a-6356eb2ed1aa · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Graph Mamba: Towards Learning on Graphs with State Space Models

Reference 9

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This paper cites Learning long-term dependencies with gradient descent is difficult.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Learning long-term dependencies with gradient descent is difficult

Reference 10

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Observation f9cd668b-6eef-45e7-9b51-4f5488e8d875 · outbound

This paper cites Graph neural networks with convolutional arma filters.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Graph neural networks with convolutional arma filters

Reference 11

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Beyond low-frequency information in graph convolutional networks

Reference 12

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This paper cites Improving graph neural network expressivity via subgraph isomorphism counting.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Improving graph neural network expressivity via subgraph isomorphism counting

Reference 13

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Observation b259476e-d013-4675-bc9c-7a3607781a61 · outbound

This paper cites Time Series Analysis: Forecasting and Control.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Time Series Analysis: Forecasting and Control

Reference 14

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Observation 71b5dbcc-959d-4c8a-8141-a750bf5efeed · outbound

This paper cites Residual Gated Graph ConvNets.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Residual Gated Graph ConvNets

Reference 15

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Observation 80879b35-6f70-43ef-bb10-00fd922b619c · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 16

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Observation 2f13268f-4244-4bc2-8e2a-7d903a83346c · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models A Note on Over-Smoothing for Graph Neural Networks

Reference 17

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This paper cites GRAND : Graph neural diffusion.

GRAMA: Adaptive Graph Autoregressive Moving Average Models GRAND : Graph neural diffusion

Reference 18

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This paper cites Simple and Deep Graph Convolutional Networks.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Simple and Deep Graph Convolutional Networks

Reference 19

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This paper cites Neural ordinary differential equations.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Neural ordinary differential equations

Reference 20

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Observation 6a9337bd-40b3-41ef-8616-438975a451ff · outbound

This paper cites Adaptive universal generalized pagerank graph neural network.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Adaptive universal generalized pagerank graph neural network

Reference 21

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Gread: Graph neural reaction-diffusion networks

Reference 22

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Multi-channel Deep 3D Face Recognition

Reference 23

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models From block-toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked transformers

Reference 24

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This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 25

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This paper cites The arma model in state space form.

GRAMA: Adaptive Graph Autoregressive Moving Average Models The arma model in state space form

Reference 26

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This paper cites Polynormer: Polynomial-expressive graph transformer in linear time.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Polynormer: Polynomial-expressive graph transformer in linear time

Reference 27

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Observation 7351b26f-79d9-4569-84d4-13deaee3d3e1 · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models On over-squashing in message passing neural networks: the impact of width, depth, and topology

Reference 28

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily

Reference 29

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This paper cites Dwivedi and X.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Dwivedi and X

Reference 30

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This paper cites A Generalization of Transformer Networks to Graphs.

GRAMA: Adaptive Graph Autoregressive Moving Average Models A Generalization of Transformer Networks to Graphs

Reference 31

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Graph neural networks with learnable structural and positional representations

Reference 32

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This paper cites Long Range Graph Benchmark.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Long Range Graph Benchmark

Reference 33

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This paper cites Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson

Reference 34

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This paper cites Benchmarking graph neural networks.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Benchmarking graph neural networks

Reference 35

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models PDE-GCN : Novel architectures for graph neural networks motivated by partial differential equations

Reference 36

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GRAMA: Adaptive Graph Autoregressive Moving Average Models GRANOLA: Adaptive Normalization for Graph Neural Networks

Reference 37

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models On the temporal domain of differential equation inspired graph neural networks

Reference 38

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This paper cites Bronstein, and Ismail Ilkan Ceylan.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Bronstein, and Ismail Ilkan Ceylan

Reference 39

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models A large-scale database for graph representation learning

Reference 40

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This paper cites S4: Structured state space for scalable and efficient sequence modeling.

GRAMA: Adaptive Graph Autoregressive Moving Average Models S4: Structured state space for scalable and efficient sequence modeling

Reference 41

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This paper cites Diffusion Improves Graph Learning.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Diffusion Improves Graph Learning

Reference 42

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Anti-Symmetric DGN: a stable architecture for Deep Graph Networks

Reference 43

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This paper cites On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems.

GRAMA: Adaptive Graph Autoregressive Moving Average Models On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems

Reference 44

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Temporal graph odes for irregularly-sampled time series

Reference 45

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This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Efficiently Modeling Long Sequences with Structured State Spaces

Reference 46

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This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 47

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Unresolved cited work

Reference 48

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Unresolved cited work

Reference 49

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Drew: Dynamically rewired message passing with delay

Reference 50

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Hamilton

Reference 51

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GRAMA: Adaptive Graph Autoregressive Moving Average Models State-space models

Reference 52

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Hamilton, Rex Ying, and Jure Leskovec

Reference 53

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Deep residual learning for image recognition

Reference 54

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Bounding the roots of polynomials

Reference 55

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This paper cites Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001

Reference 56

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This paper cites Matrix analysis.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Matrix analysis

Reference 57

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This paper cites Open graph benchmark: Datasets for machine learning on graphs.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Open graph benchmark: Datasets for machine learning on graphs

Reference 58

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Strategies for Pre-training Graph Neural Networks

Reference 59

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Densely connected convolutional networks

Reference 60

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This paper cites What Can We Learn from State Space Models for Machine Learning on Graphs?.

GRAMA: Adaptive Graph Autoregressive Moving Average Models What Can We Learn from State Space Models for Machine Learning on Graphs?

Reference 61

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Autoregressive moving average graph filtering

Reference 62

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This paper cites Unleashing the potential of fractional calculus in graph neural networks with FROND.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Unleashing the potential of fractional calculus in graph neural networks with FROND

Reference 63

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This paper cites Banerjee, and Guido Montufar.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Banerjee, and Guido Montufar

Reference 64

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Reference 65

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This paper cites A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions.

GRAMA: Adaptive Graph Autoregressive Moving Average Models A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

Reference 66

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Observation aba591e2-fd4c-478f-92cf-38e3bb99e8de · outbound

This paper cites Kipf and M.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Kipf and M

Reference 67

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Observation aad84fb9-8fcf-4e9f-9f30-61c09ea8811e · outbound

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Bayan Bruss, and Tom Goldstein

Reference 68

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This paper cites Rethinking graph transformers with spectral attention.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Rethinking graph transformers with spectral attention

Reference 69

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Kreuzer et al

Reference 70

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Observation 19bfeea3-f216-4677-9381-e68a7854e7bd · outbound

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-10T16:57:30.603850Z

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Finding global homophily in graph neural networks when meeting heterophily

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.592579Z

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Observation cc4fcfc0-42b4-4548-a9e4-e33da8174b03 · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Toloker Graph: Interaction of Crowd Annotators , February 2023

Reference 73

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Mamba: Beyond long sequences

Reference 74

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Observation eb1069b7-e0ba-4f4d-b9d6-dfa85f8c57b6 · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges

Reference 75

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This paper cites DiGRAF: Diffeomorphic Graph-Adaptive Activation Function.

GRAMA: Adaptive Graph Autoregressive Moving Average Models DiGRAF: Diffeomorphic Graph-Adaptive Activation Function

Reference 76

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This paper cites A fractional graph laplacian approach to oversmoothing.

GRAMA: Adaptive Graph Autoregressive Moving Average Models A fractional graph laplacian approach to oversmoothing

Reference 77

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Simplifying approach to node classification in graph neural networks

Reference 78

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Unavailable: canonical work link unavailable.

Observation 3a1e4999-8f5b-4d64-b01c-bccf515ad3cd · outbound

This paper cites Weisfeiler and leman go neural: Higher-order graph neural networks.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Weisfeiler and leman go neural: Higher-order graph neural networks

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.563567Z

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Observation b666f0da-a06f-4c25-bacb-3679a60e3b0d · outbound

This paper cites Attending to graph transformers.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Attending to graph transformers

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.553299Z

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

Observation be0226fa-0ff6-4734-9aff-1acfce349bea · outbound

This paper cites Nguyen et al.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Nguyen et al

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.543373Z

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Observation 33a80971-89b9-4ede-b5f8-4a68d2b2d7d5 · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Reference 82

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Graph neural networks exponentially lose expressive power for node classification

Reference 83

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Observation a0362733-5b59-49fa-a4a4-b6fc9617f2e6 · outbound

This paper cites Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues

Reference 84

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Unavailable: canonical work link unavailable.

Observation 8d561eb9-2c98-4308-8b0a-d2803246efa5 · outbound

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Resurrecting recurrent neural networks for long sequences

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.525312Z

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

Observation 30e959f8-45bc-47e1-8da5-4d6a29e78fdb · outbound

This paper cites Permutation equivariant layers for higher order interactions.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Permutation equivariant layers for higher order interactions

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.514384Z

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

Observation 00806e71-dbd9-419c-8dab-2ba2d6a2cbbb · outbound

This paper cites On the difficulty of training Recurrent Neural Networks.

GRAMA: Adaptive Graph Autoregressive Moving Average Models On the difficulty of training Recurrent Neural Networks

Reference 87

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Observation 9685610f-4905-4335-9191-53f4c6e2095c · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Pytorch: An imperative style, high-performance deep learning library

Reference 88

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Unavailable: canonical work link unavailable.

Observation 82964e13-d986-4e0b-925e-140c2e3917a3 · outbound

This paper cites Geom-gcn: Geometric graph convolutional networks.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Geom-gcn: Geometric graph convolutional networks

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.496464Z

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

Observation 7a970a88-9b43-45b7-a1ee-a93e5a910b4b · outbound

This paper cites A critical look at the evaluation of GNN s under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023.

GRAMA: Adaptive Graph Autoregressive Moving Average Models A critical look at the evaluation of GNN s under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.484342Z

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 3996a4df-e669-4400-9de5-a386c2315615 · outbound

This paper cites Graph Neural Ordinary Differential Equations.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Graph Neural Ordinary Differential Equations

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-10T16:57:29.303679Z

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Unavailable: canonical work link unavailable.

Observation 739d60ea-51bb-4870-9cfa-519e0ac7abf4 · outbound

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GRAMA: Adaptive Graph Autoregressive Moving Average Models Recipe for a General, Powerful, Scalable Graph Transformer

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.471991Z

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

Observation 60cee00b-5595-4a34-8aff-5ca05ecda534 · outbound

This paper cites Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-10T16:57:29.309414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1df34719-7dee-4b8a-8539-bc468848d95f · outbound

This paper cites Graph-coupled oscillator networks.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Graph-coupled oscillator networks

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.459184Z

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

Observation b26d4e97-75fe-49a7-9f5c-c2decba67dec · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models A Survey on Oversmoothing in Graph Neural Networks

Reference 95

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97752ecd-9be9-4770-8fa5-248e134cdac3 · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Deep neural networks motivated by partial differential equations

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.449021Z

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

Observation 0ab199d6-7cd5-4905-8c71-d1860caeb3cc · outbound

This paper cites Theoretical guarantees for permutation-equivariant quantum neural networks.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Theoretical guarantees for permutation-equivariant quantum neural networks

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.439186Z

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 53a2f55c-e96e-424d-8a11-96f1740250f1 · outbound

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

GRAMA: Adaptive Graph Autoregressive Moving Average Models Masked label prediction: Unified message passing model for semi-supervised classification

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-10T16:57:29.325005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 852e9fc8-b8ba-4276-a987-7b3fafe02f9d · outbound

This paper cites Rahmani, and Marzieh Aghaei.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Rahmani, and Marzieh Aghaei

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-10T16:57:29.328424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a721b98-86f7-4a9d-b6d4-6de6ff97d1c7 · outbound

This paper cites Applied nonlinear control, volume 199.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Applied nonlinear control, volume 199

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:57:30.420858Z

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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Reference resolution

Outbound references

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