Paper Citation Record · LEDGER

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration

As of 18 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2502.01809.

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

pith.paper-citation-record.v1
2502.01809 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:27:33.530979Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

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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

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation 4315b5e4-9058-48ae-ba9b-43903f4e3809 · outbound

This paper cites Subgraph neural net- works.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Subgraph neural net- works

Reference 1

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Observation 5ff4e672-fd14-406b-80b2-b58aa3ebe9e6 · outbound

This paper cites Efficient Subgraph GNNs by Learning Effective Selection Policies.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Efficient Subgraph GNNs by Learning Effective Selection Policies

Reference 2

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Observation 0c9bacc9-5e84-4465-81a8-ee2f1beae748 · outbound

This paper cites Equivariant Subgraph Aggregation Networks.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Equivariant Subgraph Aggregation Networks

Reference 3

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Observation 3577788a-ad12-458e-b35b-124987f1568a · outbound

This paper cites Protein function prediction via graph kernels.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Protein function prediction via graph kernels

Reference 4

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Observation db439e3c-79c7-48ef-bd4c-114f3d59d299 · outbound

This paper cites Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds

Reference 5

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Observation 7b82f74d-4d56-42af-b8cc-fa50fc5e99ba · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Fast Graph Representation Learning with PyTorch Geometric

Reference 6

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Observation 301830f3-428e-4b79-b269-c720be2cfad8 · outbound

This paper cites Inductive representation learning on large graphs.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Inductive representation learning on large graphs

Reference 7

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Observation 62d6dd8a-d514-4abf-8212-a749fcf8af58 · outbound

This paper cites King, Stefan Kramer, and Ashwin Srinivasan.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration King, Stefan Kramer, and Ashwin Srinivasan

Reference 8

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Observation e47b8727-8bdb-4c95-974a-e34968d899ec · outbound

This paper cites Finding frequent subgraphs in longitudinal social network data using a weighted graph mining approach.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Finding frequent subgraphs in longitudinal social network data using a weighted graph mining approach

Reference 9

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Observation a30a6bf0-5c7a-4a1c-b77e-65658551f4ad · outbound

This paper cites Variational Graph Auto-Encoders.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Variational Graph Auto-Encoders

Reference 10

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Observation 95713ae2-d7f4-4f03-a31d-2524a59609da · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Semi-Supervised Classification with Graph Convolutional Networks

Reference 11

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Observation 7fc30e16-0ea7-4f3c-9e79-58cac6baea5a · outbound

This paper cites Orphicx: A causality-inspired latent variable model for interpreting graph neural networks.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Orphicx: A causality-inspired latent variable model for interpreting graph neural networks

Reference 12

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Observation 9d61f112-199c-4ffb-809f-0254bf612837 · outbound

This paper cites Parameterized explainer for graph neural network.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Parameterized explainer for graph neural network

Reference 13

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Observation 0876f81e-1697-4175-8397-056fc82dad1d · outbound

This paper cites Provably powerful graph networks.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Provably powerful graph networks

Reference 14

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Observation 6220d501-973a-49c8-9a06-6a02901e4357 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Playing Atari with Deep Reinforcement Learning

Reference 15

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Observation e603bff5-d98b-4e09-a440-9d64c4a79750 · outbound

This paper cites Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Mar- ion Neumann.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Mar- ion Neumann

Reference 16

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Observation 2db5f82d-5ba7-46fb-a918-98944ad7eaca · outbound

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

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Weisfeiler and leman go neural: Higher-order graph neural networks

Reference 17

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Observation ba437d8f-a21a-4c7c-b45d-1633922b4292 · outbound

This paper cites Biological network comparison using graphlet degree distribution.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Biological network comparison using graphlet degree distribution

Reference 18

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Observation 1285c42d-07bf-4a28-a8fe-0f1f0e7bb8b2 · outbound

This paper cites Reinforcement learning enhanced explainer for graph neural networks.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Reinforcement learning enhanced explainer for graph neural networks

Reference 19

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Observation 3de74502-b198-44d5-8ed4-2210f0f7f4aa · outbound

This paper cites Shervashidze, P.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Shervashidze, P

Reference 20

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This paper cites Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism

Reference 21

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Observation 4bf9255c-6913-46fd-9fb0-1ae44582d677 · outbound

This paper cites Spline-fitting with a genetic algorithm: A method for developing classification structure-activity relationships.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Spline-fitting with a genetic algorithm: A method for developing classification structure-activity relationships

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:27:33.941831Z

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Observation 500c7355-2bde-4ada-83ff-1107481664e0 · outbound

This paper cites Optimal transport for structured data with application on graphs.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Optimal transport for structured data with application on graphs

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:27:33.919433Z

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Observation 98d930d4-863d-4aaf-a965-6ea8cabe58a1 · outbound

This paper cites Wasserstein weisfeiler-lehman graph kernels.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Wasserstein weisfeiler-lehman graph kernels

Reference 24

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Observation e76a4eb5-e1ef-4f3b-838a-c03f924ab147 · outbound

This paper cites Graphopt: Learning optimization models of graph formation.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Graphopt: Learning optimization models of graph formation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:27:33.876988Z

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Observation 7c1313ff-4515-4835-8a63-3d913e3b2365 · outbound

This paper cites Comparison of descriptor spaces for chemical compound retrieval and classification.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Comparison of descriptor spaces for chemical compound retrieval and classification

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:27:33.859458Z

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Observation 8a5cc49c-621d-4b9a-a1a7-b985cfe06c9f · outbound

This paper cites Dynamic graph cnn for learning on point clouds.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Dynamic graph cnn for learning on point clouds

Reference 27

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This paper cites Q-learning.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Q-learning

Reference 28

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Observation 0d873a97-967e-428a-b2b8-74ea952ffad2 · outbound

This paper cites A new perspective on” how graph neural networks go beyond weisfeiler-lehman?”.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration A new perspective on” how graph neural networks go beyond weisfeiler-lehman?”

Reference 29

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Observation e991d409-b9b4-4dc8-ab7a-7ec04fa828af · outbound

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

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration How powerful are graph neural networks? In International Conference on Learning Representations (ICLR), 2019

Reference 30

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Observation c05193e9-7fb6-4cf0-b346-f0a51eec27a1 · outbound

This paper cites Deep graph kernels.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Deep graph kernels

Reference 31

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Observation 25f3e03f-37e2-4f04-b594-f742ca568c93 · outbound

This paper cites Gnnexplainer: Generating explanations for graph neural networks.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration Gnnexplainer: Generating explanations for graph neural networks

Reference 32

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Observation 9a4e7106-5e5c-4d01-be1b-cac0f625de26 · outbound

This paper cites On explainability of graph neural networks via subgraph explorations.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration On explainability of graph neural networks via subgraph explorations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:27:33.734402Z

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Observation 8944dc9b-0f06-49f7-a294-30dd72342275 · outbound

This paper cites A survey on deep graph generation: Methods and applications.

Self-supervised Subgraph Neural Network With Deep Reinforcement Walk Exploration A survey on deep graph generation: Methods and applications

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:27:33.711547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.