Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:10:19.752352Z
Paper Citation Record · LEDGER
Wide & Deep Learning for Node Classification
As of 23 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2505.02020.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
55 of 55 outbound references displayed
- verified exact0
- verified fuzzy41
- unresolved14
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 9a5fe590-93db-4fbe-89bc-b3c1e8b4e96e · outbound
This paper cites Stochastic training of graph convolutional networks with variance reduction.
Wide & Deep Learning for Node Classification Stochastic training of graph convolutional networks with variance reduction
Reference 1
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c28aa7bf-8d83-4560-811e-47e5e24ae569 · outbound
This paper cites Simple and deep graph convolutional networks.
Wide & Deep Learning for Node Classification Simple and deep graph convolutional networks
Reference 2
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation fad858a9-1f2e-4db4-ae10-37aae8bc2329 · outbound
This paper cites Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs.
Wide & Deep Learning for Node Classification Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
Reference 3
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1b49bd40-52d5-41a0-9d62-69cbb539deab · outbound
This paper cites Wide & deep learning for recommender systems.
Wide & Deep Learning for Node Classification Wide & deep learning for recommender systems
Reference 4
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 03e68c63-ae12-48ac-a64d-0c7599c12dff · outbound
This paper cites Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks.
Wide & Deep Learning for Node Classification Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Reference 5
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f54f3ea2-3bad-4a56-8160-0f32c079e178 · outbound
This paper cites Polynormer: Polynomial-expressive graph transformer in linear time.
Wide & Deep Learning for Node Classification Polynormer: Polynomial-expressive graph transformer in linear time
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2ac8fc84-c238-4732-b6f2-2dec15e8ece4 · outbound
This paper cites and Lenssen, J.
Wide & Deep Learning for Node Classification and Lenssen, J
Reference 7
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d8778e9e-7a64-4653-b656-e2c20ec2b372 · outbound
This paper cites Predict then propagate: Graph neural networks meet personalized pagerank.
Wide & Deep Learning for Node Classification Predict then propagate: Graph neural networks meet personalized pagerank
Reference 8
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 38023397-a9a1-4625-a0d5-bbbfef5fadc2 · outbound
This paper cites S., Riley, P.
Wide & Deep Learning for Node Classification S., Riley, P
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2e69d3a-5449-4fb8-a2f6-74ffc023ca31 · outbound
This paper cites L., Ying, R., and Leskovec, J.
Wide & Deep Learning for Node Classification L., Ying, R., and Leskovec, J
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d2b36570-c7f9-49e0-9e7b-1bde0179ca21 · outbound
This paper cites Deep residual learning for image recognition.
Wide & Deep Learning for Node Classification Deep residual learning for image recognition
Reference 11
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d6c68f71-153b-4d44-80d5-ed6d50afa66c · outbound
This paper cites Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning.
Wide & Deep Learning for Node Classification Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:20.578107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 22743fcf-67dd-4e64-9281-73f856056ae1 · outbound
This paper cites and Szegedy, C.
Wide & Deep Learning for Node Classification and Szegedy, C
Reference 13
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8cc9f832-78da-4777-9992-334106f43187 · outbound
This paper cites an unresolved cited work.
Wide & Deep Learning for Node Classification Unresolved cited work
Reference 14
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d410ee8d-c92d-4afc-a186-7b4a02f8b876 · outbound
This paper cites an unresolved cited work.
Wide & Deep Learning for Node Classification Unresolved cited work
Reference 15
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4735236f-a7a8-4906-94fc-ca29a0b31b72 · outbound
This paper cites Deeper insights into graph convolutional networks for semi-supervised learning.
Wide & Deep Learning for Node Classification Deeper insights into graph convolutional networks for semi-supervised learning
Reference 16
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f38e2224-0c64-43b4-be70-5ddac2f2fd9f · outbound
This paper cites One for all: Towards training one graph model for all classification tasks.
Wide & Deep Learning for Node Classification One for all: Towards training one graph model for all classification tasks
Reference 17
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c5b6732-172a-4df3-b9f7-3149b147f368 · outbound
This paper cites Towards deeper graph neural networks.
Wide & Deep Learning for Node Classification Towards deeper graph neural networks
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:20.496333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation bb5d69e5-4ab8-48af-9d6f-7692248c51ef · outbound
This paper cites Class-imbalanced graph learning without class rebalancing.
Wide & Deep Learning for Node Classification Class-imbalanced graph learning without class rebalancing
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 73f34db9-c18c-49ec-bcd3-ba5e6dd33870 · outbound
This paper cites Revisiting heterophily for graph neural networks.
Wide & Deep Learning for Node Classification Revisiting heterophily for graph neural networks
Reference 20
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4ed4e116-55e0-424e-94f5-e2ca09aba9be · outbound
This paper cites Fast graph sharpness-aware minimization for enhancing and accelerating few-shot node classification.
Wide & Deep Learning for Node Classification Fast graph sharpness-aware minimization for enhancing and accelerating few-shot node classification
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d6272d07-7895-49ab-9d2b-825154a362d9 · outbound
This paper cites Classic gnns are strong baselines: Reassessing gnns for node classification.
Wide & Deep Learning for Node Classification Classic gnns are strong baselines: Reassessing gnns for node classification
Reference 22
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ef7953d9-d300-49f7-a729-5b65841fc1f9 · outbound
This paper cites Is homophily a necessity for graph neural networks? In ICLR, 2022.
Wide & Deep Learning for Node Classification Is homophily a necessity for graph neural networks? In ICLR, 2022
Reference 23
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 436c5f2b-7d5e-4609-bf95-525134e3eed4 · outbound
This paper cites The pagerank citation ranking: Bringing order to the web.
Wide & Deep Learning for Node Classification The pagerank citation ranking: Bringing order to the web
Reference 24
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7bbf06d1-874d-4972-a09b-640a2f579fd3 · outbound
This paper cites Pytorch: An imperative style, high-performance deep learning library.
Wide & Deep Learning for Node Classification Pytorch: An imperative style, high-performance deep learning library
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 563e4d9a-cea9-42bd-9c21-13be0332a626 · outbound
This paper cites C.-C., Lei, Y., and Yang, B.
Wide & Deep Learning for Node Classification C.-C., Lei, Y., and Yang, B
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:20.352102Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b63abb02-63ff-4189-b2a3-919abe2f778e · outbound
This paper cites Multi-track message passing: Tackling oversmoothing and oversquashing in graph learning via preventing heterophily mixing.
Wide & Deep Learning for Node Classification Multi-track message passing: Tackling oversmoothing and oversquashing in graph learning via preventing heterophily mixing
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 579831dc-2f25-4783-9b87-15834efc67fd · outbound
This paper cites A critical look at the evaluation of gnns under heterophily: Are we really making progress? In ICLR, 2023.
Wide & Deep Learning for Node Classification A critical look at the evaluation of gnns under heterophily: Are we really making progress? In ICLR, 2023
Reference 28
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation fdce7b7a-c075-4a25-bc63-8be160cb429a · outbound
This paper cites P., Luu, A.
Wide & Deep Learning for Node Classification P., Luu, A
Reference 29
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d6f6da63-e2b7-4933-abe7-71ea8a138055 · outbound
This paper cites Dropedge: Towards deep graph convolutional networks on node classification.
Wide & Deep Learning for Node Classification Dropedge: Towards deep graph convolutional networks on node classification
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:20.285699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 076541e8-13dd-4209-9b80-5723f01b25a2 · outbound
This paper cites Multi-scale attributed node embedding.
Wide & Deep Learning for Node Classification Multi-scale attributed node embedding
Reference 31
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7019b0eb-8b1c-4f11-82cc-caa1d5eff507 · outbound
This paper cites E., Hinton, G.
Wide & Deep Learning for Node Classification E., Hinton, G
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f16a1155-6f97-4265-989c-2e52c9947112 · outbound
This paper cites Collective classification in network data.
Wide & Deep Learning for Node Classification Collective classification in network data
Reference 33
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 33f919aa-4266-4b8b-8a50-f91a3160c548 · outbound
This paper cites Ordered gnn: Ordering message passing to deal with heterophily and over-smoothing.
Wide & Deep Learning for Node Classification Ordered gnn: Ordering message passing to deal with heterophily and over-smoothing
Reference 34
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 96a5184e-9cd1-44d5-aa5a-7742a8c42a43 · outbound
This paper cites Dropout: a simple way to prevent neural networks from overfitting.
Wide & Deep Learning for Node Classification Dropout: a simple way to prevent neural networks from overfitting
Reference 35
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 82fa8bdf-5dba-456e-994b-50ecc4233bd9 · outbound
This paper cites N., Kaiser, L., and Polosukhin, I.
Wide & Deep Learning for Node Classification N., Kaiser, L., and Polosukhin, I
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:20.155487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3ccb4d2b-6ad2-4391-9371-6f570446f4f2 · outbound
This paper cites Graph attention networks.
Wide & Deep Learning for Node Classification Graph attention networks
Reference 37
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8f4c674b-5aad-4880-9302-28366bb3bd34 · outbound
This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.
Wide & Deep Learning for Node Classification Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Reference 38
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 17ecb678-6d5c-44e6-a989-c5364bef45c9 · outbound
This paper cites H., Fifty, C., Yu, T., and Weinberger, K.
Wide & Deep Learning for Node Classification H., Fifty, C., Yu, T., and Weinberger, K
Reference 39
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2f3ade38-7ed3-4e5d-82ee-ab638794dbcf · outbound
This paper cites Nodeformer: A scalable graph structure learning transformer for node classification.
Wide & Deep Learning for Node Classification Nodeformer: A scalable graph structure learning transformer for node classification
Reference 40
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 94d49b2c-658e-4ff1-b32f-b3da434568cb · outbound
This paper cites Sgformer: Simplifying and empowering transformers for large-graph representations.
Wide & Deep Learning for Node Classification Sgformer: Simplifying and empowering transformers for large-graph representations
Reference 41
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 83321f99-45db-482f-a7c1-7abba17e2840 · outbound
This paper cites Less is more: on the over-globalizing problem in graph transformers.
Wide & Deep Learning for Node Classification Less is more: on the over-globalizing problem in graph transformers
Reference 42
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c076decc-049a-4428-8abe-32df2c661de7 · outbound
This paper cites Representation learning on graphs with jumping knowledge networks.
Wide & Deep Learning for Node Classification Representation learning on graphs with jumping knowledge networks
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:20.051187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation eb8afc3e-b33e-449a-9187-a3ac14e09464 · outbound
This paper cites How powerful are graph neural networks? In ICLR, 2019.
Wide & Deep Learning for Node Classification How powerful are graph neural networks? In ICLR, 2019
Reference 44
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 363607a7-8247-46d0-8826-01008fd3b46a · outbound
This paper cites S., ichi Kawarabayashi, K., and Jegelka, S.
Wide & Deep Learning for Node Classification S., ichi Kawarabayashi, K., and Jegelka, S
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:20.023861Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6df331b2-825d-4538-a7e7-96fbad023ef5 · outbound
This paper cites Graph neural networks are inherently good generalizers: Insights by bridging gnns and mlps.
Wide & Deep Learning for Node Classification Graph neural networks are inherently good generalizers: Insights by bridging gnns and mlps
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:20.007653Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c0e03c30-dae8-4406-88cc-a7de5637582a · outbound
This paper cites W., and Salakhutdinov, R.
Wide & Deep Learning for Node Classification W., and Salakhutdinov, R
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:10:19.701327Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 70e50698-1552-4085-92ce-6090fd777ce0 · outbound
This paper cites Do transformers really perform bad for graph representation? In NeurIPS, 2021.
Wide & Deep Learning for Node Classification Do transformers really perform bad for graph representation? In NeurIPS, 2021
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:19.972084Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 0024199c-f419-45ee-8324-bf6bd633b97d · outbound
This paper cites A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests.
Wide & Deep Learning for Node Classification A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:19.951694Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 53c0e16d-696a-4a4f-825d-85f378bacce5 · outbound
This paper cites Rethinking the expressive power of gnns via graph biconnectivity.
Wide & Deep Learning for Node Classification Rethinking the expressive power of gnns via graph biconnectivity
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:19.932348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 37c983f8-d0ef-4d82-80b3-e0e222884fdc · outbound
This paper cites Evaluating Deep Graph Neural Networks.
Wide & Deep Learning for Node Classification Evaluating Deep Graph Neural Networks
Reference 51
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 80c8820d-172c-48ea-8ce2-a318268fdc43 · outbound
This paper cites Online gnn evaluation under test-time graph distribution shifts.
Wide & Deep Learning for Node Classification Online gnn evaluation under test-time graph distribution shifts
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:19.905656Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 5dbf6c64-99f8-4dd9-8231-ee0f77680cb2 · outbound
This paper cites What is missing in homophily? disentangling graph homophily for graph neural networks.
Wide & Deep Learning for Node Classification What is missing in homophily? disentangling graph homophily for graph neural networks
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:19.887066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 583234e8-548e-4684-b626-be1741e23d26 · outbound
This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.
Wide & Deep Learning for Node Classification Beyond homophily in graph neural networks: Current limitations and effective designs
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T04:10:19.870197Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 0be06b8b-24f1-4d3b-819f-8afffd103539 · outbound
This paper cites write newline.
Wide & Deep Learning for Node Classification write newline
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:10:19.752352Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.