Paper Citation Record · LEDGER

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

As of 14 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:1908.08402.

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

pith.paper-citation-record.v1
1908.08402 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:02:20.632825Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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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

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 90628288-406b-4322-92e9-f0714e692256 · outbound

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

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Semi-supervised classiﬁcation with graph convolutional networks,

Reference 1

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This paper cites Egonet: identiﬁcation of human disease ego-network modules,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Egonet: identiﬁcation of human disease ego-network modules,

Reference 2

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This paper cites Moleculenet: a benchmark for molecular machine learning,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Moleculenet: a benchmark for molecular machine learning,

Reference 3

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This paper cites Interaction networks for learning about objects, relations and physics,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Interaction networks for learning about objects, relations and physics,

Reference 4

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This paper cites node2vec : scalable feature learning for networks,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions node2vec : scalable feature learning for networks,

Reference 5

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Observation b1e070f9-8803-4fc4-b832-36ab8062cf84 · outbound

This paper cites Graph Embedding Techniques, Applications, and Performance: A Survey.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Graph Embedding Techniques, Applications, and Performance: A Survey

Reference 6

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

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Pytorch: An imperative style, high-performance deep learning library,

Reference 7

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This paper cites Laplacian eigenmaps and spectral techniques for embedding and clustering,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Laplacian eigenmaps and spectral techniques for embedding and clustering,

Reference 8

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Observation 4140d6c2-c9b9-4ff4-a651-2e9c7e883304 · outbound

This paper cites Distributed large-scale natural graph factorization,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Distributed large-scale natural graph factorization,

Reference 9

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Observation 241622db-795e-4fe6-bfc0-e0e691267428 · outbound

This paper cites DeepWalk: online learning of social representations,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions DeepWalk: online learning of social representations,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:02:21.039456Z

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Observation 440b8b7f-a7c9-4239-9af5-c76cebbe7684 · outbound

This paper cites Autoencoders, unsupervised learning, and deep architectures,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Autoencoders, unsupervised learning, and deep architectures,

Reference 11

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Observation 5dafe003-5a6e-4792-81c6-789beebb36eb · outbound

This paper cites Variational Graph Auto-Encoders.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Variational Graph Auto-Encoders

Reference 12

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Observation bf6950c6-2b70-49de-b070-ff0fc7ddb187 · outbound

This paper cites Stwalk: learning trajectory representations in temporal graphs,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Stwalk: learning trajectory representations in temporal graphs,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:02:21.015564Z

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Observation e80cb1b5-328e-4fa3-9f9b-627c7f1d062a · outbound

This paper cites Netwalk: A ﬂexible deep embedding approach for anomaly detection in dynamic networks,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Netwalk: A ﬂexible deep embedding approach for anomaly detection in dynamic networks,

Reference 14

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Observation 65a636b6-fee6-475b-8149-dcdaa2828142 · outbound

This paper cites Continuous-time dynamic network embeddings,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Continuous-time dynamic network embeddings,

Reference 15

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Observation 8ad0c015-74fa-40a1-824f-00cf8ec87564 · outbound

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

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions DynGEM: Deep Embedding Method for Dynamic Graphs

Reference 16

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Observation 34dab750-ad5f-4211-825b-303942b80b3e · outbound

This paper cites Structural deep network embedding,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Structural deep network embedding,

Reference 17

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Observation 808eaa92-d94d-44de-b1d9-5515a4faed9b · outbound

This paper cites Net2Net: Accelerating Learning via Knowledge Transfer.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Net2Net: Accelerating Learning via Knowledge Transfer

Reference 18

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Observation b341f52d-25c1-48c9-ab5d-71a7590991e3 · outbound

This paper cites dyngraph2vec: Captur- ing network dynamics using dynamic graph representation learning,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions dyngraph2vec: Captur- ing network dynamics using dynamic graph representation learning,

Reference 19

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Observation dad9b5f0-ed4f-4d30-afea-60aa9ea4eb56 · outbound

This paper cites Dynamic graph convolutional networks,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Dynamic graph convolutional networks,

Reference 20

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Observation da1161f7-17eb-434a-8233-85ca49dcf4b2 · outbound

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

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Structured sequence modeling with graph convolutional recurrent networks,

Reference 21

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Observation 43faf8df-c1b1-4a10-b57c-3c7e40c8dd06 · outbound

This paper cites Gcn-gan: A non-linear temporal link prediction model for weighted dynamic networks,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Gcn-gan: A non-linear temporal link prediction model for weighted dynamic networks,

Reference 22

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Observation 75f26379-94d2-466d-b97e-0293a82934b1 · outbound

This paper cites GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction

Reference 23

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Observation 1a74fa93-a04c-4456-8863-1085eaab39f2 · outbound

This paper cites EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Reference 24

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This paper cites Revisiting spatial- temporal similarity: A deep learning framework for trafﬁc prediction,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Revisiting spatial- temporal similarity: A deep learning framework for trafﬁc prediction,

Reference 25

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

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 26

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Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Long short-term memory,

Reference 27

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This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 28

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This paper cites FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

Reference 29

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Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Inductive representation learning on large graphs,

Reference 30

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Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Layer Normalization

Reference 31

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This paper cites Deep residual learning for image recognition,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Deep residual learning for image recognition,

Reference 32

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Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Auto-Encoding Variational Bayes

Reference 33

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Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Temporal graph offset reconstruction: Towards tempo- rally robust graph representation learning,

Reference 34

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This paper cites SNAP Datasets: Stanford large network dataset collection,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions SNAP Datasets: Stanford large network dataset collection,

Reference 35

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This paper cites Konect: the koblenz network collection,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Konect: the koblenz network collection,

Reference 36

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Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Stochastic blockmodels and community structure in networks,

Reference 37

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This paper cites Deep topology classiﬁcation: A new approach for massive graph classiﬁcation,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Deep topology classiﬁcation: A new approach for massive graph classiﬁcation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:02:20.839049Z

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Observation 0769f54f-d015-4a43-ac3c-8f0cde1660ef · outbound

This paper cites Efﬁcient comparison of massive graphs through the use of graph ﬁngerprints,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Efﬁcient comparison of massive graphs through the use of graph ﬁngerprints,

Reference 39

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This paper cites DynamicGEM: A Library for Dynamic Graph Embedding Methods.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions DynamicGEM: A Library for Dynamic Graph Embedding Methods

Reference 40

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This paper cites E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Reference 41

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Observation 1473ed40-ddb5-46f4-b91d-578b4a96be3e · outbound

This paper cites Generating text with recurrent neural networks,.

Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions Generating text with recurrent neural networks,

Reference 42

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