Pith. sign in

Paper Citation Record · LEDGER

Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2402.00789.

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

pith.paper-citation-record.v1
2402.00789 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 24 of 24 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:05:31.703064Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

20
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d1583c42-1a9c-4704-b0d7-82040dc3a0e5 · inbound

Gated Linear Attention Transformers with Hardware-Efficient Training cites this paper.

Gated Linear Attention Transformers with Hardware-Efficient Training Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-05-15T01:15:14.201375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-15T01:15:13.991219Z digest=sha256:6114b2d7dd65a4cd81badf38ac878176c4e098aaa67fb3c5c319a5444ec1164b

Observation 07839e3c-0aa8-4c9b-997c-2a7325ed1662 · inbound

3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion cites this paper.

3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-24T02:13:45.158336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T02:09:11.515173Z digest=sha256:1258673cc446f60895041e3523b7adc26610dc76023bf33825e25ac1d5be21c6

Observation d4b921b0-0ecc-4a7f-83a9-bd2820c80f95 · inbound

A Survey of Mamba cites this paper.

A Survey of Mamba Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 186

Resolution
verified exact
arxiv_id, observed 2026-05-23T22:13:30.842084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T22:09:19.917854Z digest=sha256:5b50473f927dff0588bdba106d202b70d131a653bf212321dafa6de70575fe85

Observation b8aaa109-bb85-43b7-8485-ce20170ecf42 · inbound

Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space cites this paper.

Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:42:33.280514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T04:41:50.959730Z digest=sha256:704a71cf017032584a159a736e22190c7e55dfa403ac96f37ff2051a5a17142c

Observation 83f353ef-c7d7-41f5-aa0d-c0741de556aa · inbound

On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach cites this paper.

On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-09T13:05:31.703064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:05:31.703064Z digest=sha256:3a74dd44ce869b80f58a0b3493794cf9f982c6d185c1c85298dce96c645fce49

Observation ff703fdd-f92e-435b-9d6e-76155a9408b7 · inbound

From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics cites this paper.

From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-08T10:08:40.976570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T10:08:40.976570Z digest=sha256:a701e2e6a85feac3cae68f68838b84a53e346c99f27ad868d61e85b3970685dd

Observation 9c3db074-7800-43c9-952e-43b9402c5886 · inbound

ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation cites this paper.

ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-23T02:27:25.163804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-23T02:26:06.815228Z digest=sha256:2e2da64a416996c75eb1e810067744073d0717a53acd8404a1aa21db59728724

Observation 95d5399e-13ff-40ed-becd-67198954794a · inbound

Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State- Space Architectures from S4 to Mamba cites this paper.

Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State- Space Architectures from S4 to Mamba Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-22T22:12:11.773273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T22:07:12.198095Z digest=sha256:33a3d512d06830cbb031cf58f9cb80eb796f158116805b53bd3927d45d48eaf0

Observation 65f74161-5a8e-40d6-829d-5b2b657dcba4 · inbound

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling cites this paper.

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 110

Resolution
unresolved
no resolver link, observed 2026-08-07T14:32:44.170329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:32:44.170329Z digest=sha256:fbf860cbd6a27b7e1fdfcaa04247bf9973969122498f0683b767115878018897

Observation ddc805cc-c38e-49f5-b375-d5107813675b · inbound

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data cites this paper.

A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T13:40:17.997600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:40:17.997600Z digest=sha256:d1c0fa0182ff72eedb5de05a1120e3c3ec157c506774d9ec443fe6e535e76f36

Observation b0f7cf33-d500-4a19-a4e5-64ae66b43620 · inbound

On Measuring Long-Range Interactions in Graph Neural Networks cites this paper.

On Measuring Long-Range Interactions in Graph Neural Networks Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T10:18:57.082764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:18:57.082764Z digest=sha256:678cf1f5999e591c6a1ac73f5fbe691a0465ce6de8add974404d2bab545e62ea

Observation 3b41b797-7c41-4062-bad1-2d9c2699c94a · inbound

MambaHash: Visual State Space Deep Hashing Model for Large-Scale Image Retrieval cites this paper.

MambaHash: Visual State Space Deep Hashing Model for Large-Scale Image Retrieval Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:59.129702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:59.129702Z digest=sha256:ed195f902888030d49b9acc47cad4478470aa17628c1606e08cebd5266c35d28

Observation 068aaf73-41d5-4e48-a6e3-3e8655b33c0f · inbound

HydraMamba: Multi-Head State Space Model for Global Point Cloud Learning cites this paper.

HydraMamba: Multi-Head State Space Model for Global Point Cloud Learning Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T14:04:42.645025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:04:42.645025Z digest=sha256:1fe35c8e985366d581ba914c105a84d1f5c7fb69e53607102e2ad61d189ee8e6

Observation fc8e0359-c075-4c83-91ba-eb0762eca015 · inbound

Mamba-X: An End-to-End Vision Mamba Accelerator for Edge Computing Devices cites this paper.

Mamba-X: An End-to-End Vision Mamba Accelerator for Edge Computing Devices Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T04:51:34.765521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:51:34.765521Z digest=sha256:28a3887b07c7b2f448c97536e7afe1c8f228b839cdf787d6b5af280720b878fc

Observation 23c5b6e2-2445-43a7-9fa7-f015dde1ead7 · inbound

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows cites this paper.

TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 94

Resolution
unresolved
no resolver link, observed 2026-08-05T23:39:46.394973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T23:39:46.394973Z digest=sha256:cdc7a173bf308753cc6db5c15c74990b394377273cf2448c93d216cf0ded24b1

Observation d6d15e7e-4f5f-4d8a-b28b-bffb79a437d8 · inbound

eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing cites this paper.

eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-05T20:34:28.955000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:34:28.955000Z digest=sha256:2c0a94650a74375a0032eba880354b6a020044257d05285ca03593f90e99cc59

Observation 8c725671-f6bd-4758-972f-23439adb3e36 · inbound

S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning cites this paper.

S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:30:23.152652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-25T05:27:30.274067Z digest=sha256:a76f9cc86c83e81952a6e04720206bfbe5b46aeb3ff19d5594295c340d2b8254

Observation 71125664-8a4f-47b0-b962-53c2ae860ca2 · inbound

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction cites this paper.

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-06-28T19:12:33.992510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T19:10:42.461337Z digest=sha256:0f4136bac1b7b280ab585fd56f29f2e162b363392e55286c6ff90ad0d707bbc2

Observation 7a2bce99-7337-4e39-9641-3cb12f9d20f5 · inbound

Graph Mamba Survival Analysis Based on Topology-Aware ordering cites this paper.

Graph Mamba Survival Analysis Based on Topology-Aware ordering Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:14:47.099411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T15:09:00.152708Z digest=sha256:2880c7f2b7a308be353cafd07fd23eb7562e53d5346c98b1bc79b926a3895996

Observation 0d5f3c4c-a4d2-4276-8090-639bc5c96e7b · inbound

SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning cites this paper.

SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:26:46.083921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T06:55:57.778513Z digest=sha256:efc9fcfe8865191ffd1713d0581aa37064e8c91eef27f07f777d075afae90391

Observation a5b9ecb8-7efe-4fd8-8b3c-a29194731f54 · inbound

Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems cites this paper.

Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 95

Resolution
verified exact
arxiv_id, observed 2026-07-03T00:37:29.978032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-27T17:07:27.417845Z digest=sha256:e3d88076d87415e42cc8b2e803530744fb982577126b1a2d4c4d2ac6c91a033b

Observation bb9bdf6b-23e9-4b98-87db-eab1c8b93949 · inbound

State Space Models Meet Remote Sensing: A Survey cites this paper.

State Space Models Meet Remote Sensing: A Survey Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 155

Resolution
verified exact
arxiv_id, observed 2026-07-04T19:30:07.488230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-25T21:18:05.054587Z digest=sha256:3e49b82b26437fa383aa42a7f27e53df98a9698612c5fb60efd644ab3234ce27

Observation 673d31d4-4d92-4ec6-b0ec-aa07a311efdf · inbound

Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis cites this paper.

Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 140

Resolution
verified exact
local_arxiv, observed 2026-07-09T20:46:33.695916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-09T20:37:24.192337Z digest=sha256:d3b58a33098fb8c270033815a851c6663767c00ef65b946afc9ac86ae37929ad

Observation b8cc38de-0338-40ca-9aa5-c7638eaf20ee · inbound

Benchmarking Sheaf Neural Networks for Inductive Tasks cites this paper.

Benchmarking Sheaf Neural Networks for Inductive Tasks Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-04T04:52:32.579447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:52:32.579447Z digest=sha256:2e8028476a402c9c473f1aa6bd5e1d9ed17f1a6c0c143686180008d99ac7bf40