Paper Citation Record · LEDGER

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond

As of 22 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2502.06126.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.06126 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:47:18.712008Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy4
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 28f0356a-a658-4b60-b5f9-d005185d8cd0 · outbound

This paper cites Additionally, prior works in neural stochastic differential equations [Kidger et al., 2021] enforce similar regularization strategies to maintain numerical sta- bility.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Additionally, prior works in neural stochastic differential equations [Kidger et al., 2021] enforce similar regularization strategies to maintain numerical sta- bility

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:47:18.843320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07590a91-3eba-47a4-b583-9016301cab38 · outbound

This paper cites an unresolved cited work.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:47:18.865503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2b97ffa6-dedf-4cbc-a7c6-b074cf05ee5b · outbound

This paper cites Inductive Representation Learning on Temporal Graphs.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Inductive Representation Learning on Temporal Graphs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T16:47:18.683579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0227948-e2f2-4eb6-b284-762b7bf45dcc · outbound

This paper cites Equivariant Graph Neural Operator for Modeling 3D Dynamics.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Equivariant Graph Neural Operator for Modeling 3D Dynamics

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T16:47:18.687819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c989d49b-eaef-4e7d-9450-5733ed0637cd · outbound

This paper cites For the hyperparameters, we let the hidden dimension of the GCN model as 64 and the dropout ratio as 0.5 with the learning rate as 1e−3 and weight decay as 1e−4.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond For the hyperparameters, we let the hidden dimension of the GCN model as 64 and the dropout ratio as 0.5 with the learning rate as 1e−3 and weight decay as 1e−4

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:47:18.830548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aa08fd24-d507-417c-b53f-07ff01b3e36e · outbound

This paper cites We start by discretizing the SDE with a small step ∆t, we obtain: x(t + ∆t) = x(t) + ψθ(x(t), t)∆t + ξϕ(x(t), t)∆B(t).

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond We start by discretizing the SDE with a small step ∆t, we obtain: x(t + ∆t) = x(t) + ψθ(x(t), t)∆t + ξϕ(x(t), t)∆B(t)

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:47:18.855137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c7fe8af0-a682-428f-bac1-bf50d9700249 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-08T16:47:18.671252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40da3217-8862-40b6-9d07-6fe217592194 · outbound

This paper cites Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-08T16:47:18.691926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 471a87de-b2c0-4d76-b5ef-8915d824b007 · outbound

This paper cites By considering the so-called adjacency information stored in the graph, GNNs propagate graph node features by aggregating its neighboring information [Wu et al., 2020].

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond By considering the so-called adjacency information stored in the graph, GNNs propagate graph node features by aggregating its neighboring information [Wu et al., 2020]

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:47:18.876128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 884f65f4-09a4-4928-a5de-714c618d9ade · outbound

This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-08T16:47:18.667367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8bceb36-b9ce-4853-bb0c-3a1832043c1b · outbound

This paper cites Variational Graph Auto-Encoders.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Variational Graph Auto-Encoders

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-08T16:47:18.662812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f686c8a-3c6f-4520-be3d-236f7f7ce4f9 · outbound

This paper cites A Graph Autoencoder Approach to Causal Structure Learning.

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond A Graph Autoencoder Approach to Causal Structure Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T16:47:18.675026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad602e2f-2219-44cf-9261-c25fd8ef3df9 · outbound

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

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T16:47:18.679239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.