Paper Citation Record · LEDGER

Catch Causal Signals from Edges for Label Imbalance in Graph Classification

As of 11 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2501.01707.

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

pith.paper-citation-record.v1
2501.01707 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:25:28.371151Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

39 of 39 outbound references displayed

verified exact1
verified fuzzy25
unresolved13
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 59c71be9-f49d-4c90-8ac3-c8065f56ebbe · outbound

This paper cites The graph neural network model,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification The graph neural network model,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.233199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4d59663-c671-4eff-a759-30700cdad3a5 · outbound

This paper cites A comprehensive survey on graph neural networks,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification A comprehensive survey on graph neural networks,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.237716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14202b3a-8d6c-4929-b79f-96e05f3a3c68 · outbound

This paper cites Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.861367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9c72e038-8ce5-4d2e-be85-197715301f99 · outbound

This paper cites Analyzing learned molecular representations for property prediction,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Analyzing learned molecular representations for property prediction,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.849752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 96395d32-86a4-4602-a858-a2bd88107ddc · outbound

This paper cites Convolutional networks on graphs for learning molecular fingerprints,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Convolutional networks on graphs for learning molecular fingerprints,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.248958Z

Source-reported events for the cited work

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Observation 668b5024-9cc5-47e9-ba11-7fe07b554397 · outbound

This paper cites Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.832100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f076f21f-93be-4433-886f-f41c437a46d3 · outbound

This paper cites Prediction of protein–protein interaction using graph neural networks,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Prediction of protein–protein interaction using graph neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.821565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b657c3de-24a9-4d40-b6d8-7f0fc10dea08 · outbound

This paper cites Imbalanced graph classification via graph-of-graph neural networks,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Imbalanced graph classification via graph-of-graph neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.810527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c74142c4-776a-4b71-ad74-fa43911a72ba · outbound

This paper cites Graphsmote: Imbalanced node classification on graphs with graph neural networks,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Graphsmote: Imbalanced node classification on graphs with graph neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.799913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e65a1a07-a48c-4ea6-9053-21b0f83fdae5 · outbound

This paper cites Multi-class imbalanced graph convolutional network learning,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Multi-class imbalanced graph convolutional network learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.788426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4944ea79-36be-4c99-8aef-dff577090c3c · outbound

This paper cites Ins-gnn: Improving graph imbalance learning with self-supervision,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Ins-gnn: Improving graph imbalance learning with self-supervision,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.776905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 29c73a87-802c-4740-8865-6c7469342410 · outbound

This paper cites Good: A graph out-of-distribution benchmark,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Good: A graph out-of-distribution benchmark,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.765008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 67dc6b3d-b135-4079-8c8f-ca119e99591c · outbound

This paper cites A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.279169Z

Source-reported events for the cited work

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Observation 3aab1fbc-e114-4a41-828e-bc9ec3366a4e · outbound

This paper cites Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.282523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89b9d91f-f9a1-45e0-a5dc-dd06471460ad · outbound

This paper cites Trustworthy graph neural networks: aspects, methods, and trends,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Trustworthy graph neural networks: aspects, methods, and trends,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.755246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cf9bdd48-1b5c-4db8-8b4e-ce3a05d30c5b · outbound

This paper cites Learning substructure invariance for out-of-distribution molecular representations,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Learning substructure invariance for out-of-distribution molecular representations,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.745229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 58710fa8-0c6f-49f4-81d6-6198f3fff57d · outbound

This paper cites Environment-aware dynamic graph learning for out-of-distribution generalization,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Environment-aware dynamic graph learning for out-of-distribution generalization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.734301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a57ad47c-9608-45b2-8828-d56d3a849412 · outbound

This paper cites Joint learning of label and environment causal independence for graph out-of-distribution general- ization,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Joint learning of label and environment causal independence for graph out-of-distribution general- ization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.723643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a187d877-ae97-4753-af1c-4c5fea0eac50 · outbound

This paper cites Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.299482Z

Source-reported events for the cited work

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Observation 6fff8170-dd95-492d-8473-e616207c8b21 · outbound

This paper cites Towards human-like perception: Learning structural causal model in heterogeneous graph,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Towards human-like perception: Learning structural causal model in heterogeneous graph,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.712955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 929bb928-e2c2-4bad-8491-591b53b9507d · outbound

This paper cites Learning causally invariant representations for out-of- distribution generalization on graphs,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Learning causally invariant representations for out-of- distribution generalization on graphs,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.702071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ece2fd2c-24e1-48e8-9050-2f4d506e77aa · outbound

This paper cites Generalizing graph neural networks on out-of-distribution graphs,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Generalizing graph neural networks on out-of-distribution graphs,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.690354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3c0a8bd7-67ad-4c39-84c3-dcc81152cd70 · outbound

This paper cites Causal discovery with attention- based convolutional neural networks,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Causal discovery with attention- based convolutional neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.678533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1539d287-3c39-45e1-80a5-0cdc49ef2abd · outbound

This paper cites Causal attention for interpretable and generalizable graph classification,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Causal attention for interpretable and generalizable graph classification,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.666901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8805d2ba-b70f-4f6b-9066-da8e73f05110 · outbound

This paper cites Exploiting edge features for graph neural networks,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Exploiting edge features for graph neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.654945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 739907d9-96c8-4b7d-b4cd-6d2aa13827a5 · outbound

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

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Strategies for Pre-training Graph Neural Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.324361Z

Source-reported events for the cited work

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Observation 85293ccd-0f44-4463-8a1a-63dcbce8adcd · outbound

This paper cites Egat: Edge-featured graph attention network,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Egat: Edge-featured graph attention network,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.643241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation aa66604d-22ea-4ff6-bc23-1bf64fa70c4a · outbound

This paper cites Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network,

Reference 28

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Source-reported events for the cited work

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Observation f545e2b6-0a7b-4dd2-83e7-e9afa81598ba · outbound

This paper cites Do transformers really perform badly for graph representation?.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Do transformers really perform badly for graph representation?

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.625379Z

Source-reported events for the cited work

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Observation 0b93bc01-e904-4961-8e2e-ded47d635ccd · outbound

This paper cites Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor speci- ficity in rossmann fold proteins,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor speci- ficity in rossmann fold proteins,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.613212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c365880f-fa85-4b59-8f48-b308f11dcc9a · outbound

This paper cites Pearl, Causality.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Pearl, Causality

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.342672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76eb62b8-65a3-4457-8104-43c05470f995 · outbound

This paper cites Causal effect identification by adjustment under confounding and selection biases,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Causal effect identification by adjustment under confounding and selection biases,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.595122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1e3efdca-dce2-46b5-806d-92bf0d3e5895 · outbound

This paper cites Generalized adjustment under confounding and selection biases,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Generalized adjustment under confounding and selection biases,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.584220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 23c36c58-5db4-47bb-8c9c-b5d94078add6 · outbound

This paper cites Deeptox: toxicity prediction using deep learning,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Deeptox: toxicity prediction using deep learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:25:28.573269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b04238dc-6e8f-4957-85d5-75f2d3081ca7 · outbound

This paper cites Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs,.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.356238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d9b9726-1fd3-4405-8f32-ccd466b0c899 · outbound

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

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Open graph benchmark: Datasets for machine learning on graphs,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.359682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bb2e329-a0ec-464d-adcb-4772d9504e15 · outbound

This paper cites Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:25:28.429596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ca577563-dff4-4645-a548-a693ab1ef6cb · outbound

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

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Semi-Supervised Classification with Graph Convolutional Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.367198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 256b20cd-ec1c-48e9-900d-1f719fd7e441 · outbound

This paper cites Graph Attention Networks.

Catch Causal Signals from Edges for Label Imbalance in Graph Classification Graph Attention Networks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T22:25:28.371151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.