Paper Citation Record · LEDGER

Variational Graph Recurrent Neural Networks

As of 16 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:1908.09710.

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

pith.paper-citation-record.v1
1908.09710 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:08:51.096043Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T12:02:12.857242Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T12:05:22.255725Z

Reference resolution

27 of 27 outbound references displayed

verified exact1
verified fuzzy17
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7fd16b01-f320-4573-9a7e-44d2c7578291 · outbound

This paper cites Robust negative sam- pling for network embedding.

Variational Graph Recurrent Neural Networks Robust negative sam- pling for network embedding

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.687900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 683e3320-d248-4561-b565-9e11eb68c16f · outbound

This paper cites Deep gaussian embedding of graphs: Unsuper- vised inductive learning via ranking.

Variational Graph Recurrent Neural Networks Deep gaussian embedding of graphs: Unsuper- vised inductive learning via ranking

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.668680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c0c63f34-746b-4ee3-9528-2a4a1834d9a0 · outbound

This paper cites A recurrent latent variable model for sequential data.

Variational Graph Recurrent Neural Networks A recurrent latent variable model for sequential data

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.630604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3260baf4-af06-4f39-9c28-c7bccf9b7671 · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral ﬁltering.

Variational Graph Recurrent Neural Networks Convolutional neural networks on graphs with fast localized spectral ﬁltering

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:50.939701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d199d37a-3126-4001-9666-beb323a4ba45 · outbound

This paper cites Learning structural node embeddings via diffusion wavelets.

Variational Graph Recurrent Neural Networks Learning structural node embeddings via diffusion wavelets

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.599316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e2cab03b-ede7-44b3-8816-28e3bfad911a · outbound

This paper cites Sequential neural models with stochastic layers.

Variational Graph Recurrent Neural Networks Sequential neural models with stochastic layers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.581662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 162c2a6b-4663-4825-a894-65417e1feaaa · outbound

This paper cites Sequential neural models with stochastic layers.

Variational Graph Recurrent Neural Networks Sequential neural models with stochastic layers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:50.958622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf367422-c449-4a50-a7aa-8f67d7b63893 · outbound

This paper cites Z-forcing: Training stochastic recurrent networks.

Variational Graph Recurrent Neural Networks Z-forcing: Training stochastic recurrent networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.549204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8229424c-69f0-4f51-9769-0313d91ad3a7 · outbound

This paper cites Z-forcing: Training stochastic recurrent networks.

Variational Graph Recurrent Neural Networks Z-forcing: Training stochastic recurrent networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.531870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9a92998c-f1a4-4217-ba5c-c11e6c838c9a · outbound

This paper cites DynGEM: Deep Embedding Method for Dynamic Graphs.

Variational Graph Recurrent Neural Networks DynGEM: Deep Embedding Method for Dynamic Graphs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:50.979920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e08132dd-c695-414d-aab7-d5aa58c557db · outbound

This paper cites dyngraph2vec: Capturing network dynamics using dynamic graph representation learning.

Variational Graph Recurrent Neural Networks dyngraph2vec: Capturing network dynamics using dynamic graph representation learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.513547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c88844d0-3add-4996-b776-6a50c614e112 · outbound

This paper cites node2vec: Scalable feature learning for networks.

Variational Graph Recurrent Neural Networks node2vec: Scalable feature learning for networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:50.995000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e335d4a-9ed4-48a3-9fa2-05462d304fd9 · outbound

This paper cites Inductive representation learning on large graphs.

Variational Graph Recurrent Neural Networks Inductive representation learning on large graphs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:51.001771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82aacfdb-4a85-4eca-8fe2-2b2c7d133aa9 · outbound

This paper cites Variational Graph Auto-Encoders.

Variational Graph Recurrent Neural Networks Variational Graph Auto-Encoders

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:51.009195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e21a2d2-8493-4ad9-94e4-6eb9701718fe · outbound

This paper cites Semi-supervised classiﬁcation with graph convolutional networks.

Variational Graph Recurrent Neural Networks Semi-supervised classiﬁcation with graph convolutional networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.467992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd962c96-2b01-4d89-be82-456af6f37c24 · outbound

This paper cites Attributed network embedding for learning in a dynamic environment.

Variational Graph Recurrent Neural Networks Attributed network embedding for learning in a dynamic environment

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.447932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 06c7aea0-f2ba-47e4-917a-f2c59e193187 · outbound

This paper cites Doubly Semi-Implicit Variational Inference.

Variational Graph Recurrent Neural Networks Doubly Semi-Implicit Variational Inference

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-14T11:08:51.165393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 04bbbceb-657b-45cf-9b2a-1fe0da884628 · outbound

This paper cites Automatic differentiation in pytorch.

Variational Graph Recurrent Neural Networks Automatic differentiation in pytorch

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:51.036761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3abe68c-d64a-4cd3-b6fb-7202deaaf9a7 · outbound

This paper cites Deepwalk: Online learning of social repre- sentations.

Variational Graph Recurrent Neural Networks Deepwalk: Online learning of social repre- sentations

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.405221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7c67a4e7-fe39-4169-9165-015ed252c1c7 · outbound

This paper cites struc2vec: Learning node representations from structural identity.

Variational Graph Recurrent Neural Networks struc2vec: Learning node representations from structural identity

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.331631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3f3a2ff1-98f2-42f2-9d01-9688830ddc84 · outbound

This paper cites Structured sequence modeling with graph convolutional recurrent networks.

Variational Graph Recurrent Neural Networks Structured sequence modeling with graph convolutional recurrent networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.308541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8525f1f1-a184-4be4-88b0-409f124b289e · outbound

This paper cites Variational Bi-LSTMs.

Variational Graph Recurrent Neural Networks Variational Bi-LSTMs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:51.063811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbbde719-7212-4937-a7bf-dca34e7b83e7 · outbound

This paper cites Line: Large- scale information network embedding.

Variational Graph Recurrent Neural Networks Line: Large- scale information network embedding

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.287111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0dbfb86b-bca1-456e-88f0-1f8e1273d1b9 · outbound

This paper cites Dyrep: Learning representations over dynamic graphs.

Variational Graph Recurrent Neural Networks Dyrep: Learning representations over dynamic graphs

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.266382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d2413020-cb41-4beb-8297-d65c324ffc30 · outbound

This paper cites Semi-implicit variational inference.

Variational Graph Recurrent Neural Networks Semi-implicit variational inference

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.246570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ebdea33e-d3d6-4079-83fb-282284b2a5c7 · outbound

This paper cites Dynamic network embedding by modeling triadic closure process.

Variational Graph Recurrent Neural Networks Dynamic network embedding by modeling triadic closure process

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:51.225300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3ef5595a-8c0d-4066-9797-5a693f7f718b · outbound

This paper cites an unresolved cited work.

Variational Graph Recurrent Neural Networks Unresolved cited work

Reference 2018

Resolution: unresolved; raw_fallback, observed 2026-08-14T11:08:51.650517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 26087d1e-8147-49ac-9534-02c279ffce2a · inbound

Learning Ad Hoc Network Dynamics via Graph-Structured World Models cites this paper.

Learning Ad Hoc Network Dynamics via Graph-Structured World Models Variational Graph Recurrent Neural Networks

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:05:22.258205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.