Paper Citation Record · LEDGER

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies

As of 18 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2509.01381.

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

pith.paper-citation-record.v1
2509.01381 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:43:08.592390Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T21:27:50.941166Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:37:18.965886Z

Reference resolution

23 of 23 outbound references displayed

verified exact2
verified fuzzy15
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f24f194-683f-4e59-8d68-099c4f93bb23 · outbound

This paper cites A gentle introduction to deep learning for graphs,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies A gentle introduction to deep learning for graphs,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.992377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 466ef85f-d1ce-420c-b839-c1e85b564e5c · outbound

This paper cites The graph neural network model,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies The graph neural network model,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:08.538697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91504fca-2b38-4896-b0e0-e1584b6fbf47 · outbound

This paper cites Neural network for graphs: A contextual constructive approach,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Neural network for graphs: A contextual constructive approach,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.978887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7b6ebc21-a7a4-4787-a256-9da2bd99d35d · outbound

This paper cites On the bottleneck of graph neural networks and its practical implications,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies On the bottleneck of graph neural networks and its practical implications,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.970915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 888feec7-13c4-4b9b-9498-c3680396989a · outbound

This paper cites Hierarchical graph neural nets can capture long-range interactions,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Hierarchical graph neural nets can capture long-range interactions,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.962492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0f92f644-3462-4a57-9b6e-932e22b18cd5 · outbound

This paper cites Hignn: Hierarchical informative graph neural networks for molecular property prediction equipped with feature-wise attention,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Hignn: Hierarchical informative graph neural networks for molecular property prediction equipped with feature-wise attention,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.953708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 50e60b4b-5217-47f5-849b-a16d2f5d8104 · outbound

This paper cites Megraph: capturing long-range interactions by alternating local and hierarchical aggregation on multi-scaled graph hierarchy,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Megraph: capturing long-range interactions by alternating local and hierarchical aggregation on multi-scaled graph hierarchy,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.945195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ce94677-5c75-4789-8496-3a28c8f2489f · outbound

This paper cites Graph neural networks with learnable structural and positional representations,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Graph neural networks with learnable structural and positional representations,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.936608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84e09d98-57a1-4027-b388-9234cedc4871 · outbound

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

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Deepwalk: online learning of social representations,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:08.557397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f705840-58aa-48cc-939a-0df227f4e0a0 · outbound

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

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies node2vec: Scalable feature learning for networks,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:08.559830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2ad18c4-560b-480a-aa05-1304b23235a8 · outbound

This paper cites Agent-based graph neural networks,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Agent-based graph neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.927711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation af6e278b-809d-4c7a-9ca3-44d19a92f4ad · outbound

This paper cites Walking out of the weisfeiler leman hierarchy: Graph learning beyond message passing,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Walking out of the weisfeiler leman hierarchy: Graph learning beyond message passing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.919449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cb99bc39-0b39-467e-8181-b31c829c19d7 · outbound

This paper cites Graph mamba: Towards learning on graphs with state space models,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Graph mamba: Towards learning on graphs with state space models,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:08.567602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4353496a-a967-424a-b236-50d3c301c83e · outbound

This paper cites Learning long range dependencies on graphs via random walks,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Learning long range dependencies on graphs via random walks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.909675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 92c7d74e-42f3-4be9-8e16-90b2f5e7e129 · outbound

This paper cites Revisiting random walks for learning on graphs,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Revisiting random walks for learning on graphs,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.900374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6e4b2278-35b4-4e00-b11a-1f6198aa705a · outbound

This paper cites Non-convolutional graph neural networks.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Non-convolutional graph neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.891618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c1d8ce7f-cade-4396-aa77-8fc6cb20e7a9 · outbound

This paper cites A fast and high quality multilevel scheme for partitioning irregular graphs,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies A fast and high quality multilevel scheme for partitioning irregular graphs,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:08.577924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2d66817-366b-448f-a555-d9d035ad9956 · outbound

This paper cites Next Level Message-Passing with Hierarchical Support Graphs.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Next Level Message-Passing with Hierarchical Support Graphs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:08.580291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cadb937-67cf-4ce9-84cb-56e5951b772c · outbound

This paper cites Graph pooling for graph neural networks: progress, challenges, and opportunities,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Graph pooling for graph neural networks: progress, challenges, and opportunities,

Reference 19

Resolution: verified exact; doi, observed 2026-08-05T12:43:08.616811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1a9f2346-ebaf-418a-9c61-1804400ca5c5 · outbound

This paper cites Deep sets,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Deep sets,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.883034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0076ad6e-e572-43aa-8b67-4d80902b36c6 · outbound

This paper cites Categorical reparame- terization with gumbel-softmax,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Categorical reparame- terization with gumbel-softmax,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.874575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 172c14e3-98c8-4647-935d-ffeab937cf22 · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces,.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Mamba: Linear-time sequence modeling with selective state spaces,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:43:08.865791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4eb26b38-a72b-4928-aff9-980043235bb9 · outbound

This paper cites Flood and Echo Net: Algorithmically Aligned GNNs that Generalize.

Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies Flood and Echo Net: Algorithmically Aligned GNNs that Generalize

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:43:08.638787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation f4f168e6-2ab1-43e4-a126-2edaf178fde7 · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies

Reference 136

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:18.967153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.