Pith. sign in

Paper Citation Record · LEDGER

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks

As of 20 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2508.20597.

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

pith.paper-citation-record.v1
2508.20597 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:02:52.070817Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

53 of 53 outbound references displayed

  • verified exact0
  • verified fuzzy49
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 293306bd-01b2-48a2-8623-f465c97a09b2 · outbound

This paper cites Lignn: Graph neural networks at linkedin,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Lignn: Graph neural networks at linkedin,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:07.435513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:43.304748Z digest=sha256:b4841f50ad9a16dbe0768a20c0be509df1e9cb8fa4fec7a5af4a090185b7cbf9

Observation 95166cee-a8ff-4625-ac8c-7f551a958531 · outbound

This paper cites A graph and attentive multi-path convolutional network for traffic prediction,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks A graph and attentive multi-path convolutional network for traffic prediction,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:07.134744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:43.490757Z digest=sha256:e26a608e4bb840223f9ca11b8cfb371780a306315a59c76f9390ff68b21eed48

Observation f7913b4f-b41d-4296-9d43-fbee910e9329 · outbound

This paper cites Gnngo3d: Protein function prediction based on 3d structure and functional hierarchy learning,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Gnngo3d: Protein function prediction based on 3d structure and functional hierarchy learning,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:06.750833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:43.669613Z digest=sha256:d88e8d8a8c191021172820fd838a5aabb6adf07ff5e959c53191a066e8367dd3

Observation 99ea3395-8047-4f5d-aee7-dcc742c1ad90 · outbound

This paper cites Neural message passing for quantum chemistry,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Neural message passing for quantum chemistry,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:06.576665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:43.884747Z digest=sha256:b2c6ab5d4f12bafc06df0f4b6d384370f12ed7eeec5e8eae05020f437e4f9844

Observation 86852589-8f3e-4524-bbd9-a18147aba45d · outbound

This paper cites Inductive representation learning on large graphs,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Inductive representation learning on large graphs,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:06.334742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:44.106116Z digest=sha256:45034d454861b5a81917e827b6c1645bb15cc678ea68e75b30e5de0e643be39e

Observation c0e41d01-f3a5-46f0-81d5-8237dd0fbb42 · outbound

This paper cites Deeper insights into graph convolu- tional networks for semi-supervised learning,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Deeper insights into graph convolu- tional networks for semi-supervised learning,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:05.941562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:44.374873Z digest=sha256:234f4c8bda68b91f72833aa2f4a5b7f4b162d39905f6eadc9d9a85b0db767160

Observation b27be4ef-15e6-4988-93e1-80584e5fa6e5 · outbound

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

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks On the bottleneck of graph neural networks and its practical implications,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:05.500721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:44.584848Z digest=sha256:64b8f7b84f4cac72604c8af8c5e40fea9b86e0cc79df8a9c328223c6bbb877ca

Observation 2facf677-85dd-460e-be01-59e1df6b2d17 · outbound

This paper cites Graph-coupled oscillator networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Graph-coupled oscillator networks,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:05.065024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:44.766573Z digest=sha256:4e41fe30f3e0eae2723ad78f80fa6a64140620fcee1bbf19d64c256a6099a70b

Observation 2ee94866-ce37-4412-8e43-6b691464e4cc · outbound

This paper cites Towards deep attention in graph neural networks: problems and remedies,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Towards deep attention in graph neural networks: problems and remedies,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:04.637951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:45.184968Z digest=sha256:2e116224121cf18243a339abc402745d42059ec68e5c55150a1f88db7712033a

Observation 145c5140-7127-4cf9-b47d-8b508d5e23b4 · outbound

This paper cites Channel-attentive graph neural JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 12 networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Channel-attentive graph neural JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 12 networks,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:04.323469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:45.334865Z digest=sha256:38f62a24b46d4d95a00c78632dc652e6fc7a8d8406da09f844f1f1ec2e2c0b59

Observation d2990801-e3a2-4f40-a429-5841f1684370 · outbound

This paper cites Representation learning on graphs with jumping knowledge networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Representation learning on graphs with jumping knowledge networks,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:03.990086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:45.466467Z digest=sha256:bfb20362b363ba03b82ac1c615b0d30879604993e61fb88b5fe9185db1ce4005

Observation 8b6ccd25-c6d9-4710-9453-342cba7b918a · outbound

This paper cites Simple and deep graph convolutional networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Simple and deep graph convolutional networks,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:03.654816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:45.605528Z digest=sha256:1a3fbca297a42b1a70716832854392b7a266bc31ff553302162db81143e5d6b0

Observation 94bb363e-1d78-45e1-950f-a77b094eb2b0 · outbound

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

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Do transformers really perform bad for graph representation?

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:03.344802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:45.706408Z digest=sha256:be5d86b7d1da1f7837446da868b5f04e2c481285e23b6cf4fb3fd4d4d08038b0

Observation f28a3b27-98f0-4376-bf3c-8802ab31fe69 · outbound

This paper cites Graph inductive biases in transformers without message passing,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Graph inductive biases in transformers without message passing,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:02.963947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:45.844831Z digest=sha256:d32e5bc071406d270521a0d68d4852e928f08011ce26d2a539a8b2a8c4a3f055

Observation af75cf02-2354-4299-a9e2-8b9e8f241cbd · outbound

This paper cites Attending to graph transformers,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Attending to graph transformers,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:02.557553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:46.016708Z digest=sha256:f254cdeb6354016d7570e46e967c82fdc5a3cb36d83e6156107488b22dbb02fd

Observation eeb7755a-9d2a-4faa-b953-9caefc53f8aa · outbound

This paper cites Understanding over-squashing and bottlenecks on graphs via curvature,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Understanding over-squashing and bottlenecks on graphs via curvature,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:02.130368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:46.207303Z digest=sha256:d31b3cf833fb7286d57883c1a08440b6506f4d2bf57a28f5121710f6febbc2d7

Observation 2feb82a7-ece8-4a4c-8296-31d97c565edf · outbound

This paper cites FoSR: First-order spec- tral rewiring for addressing oversquashing in GNNs,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks FoSR: First-order spec- tral rewiring for addressing oversquashing in GNNs,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:01.872665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:46.305464Z digest=sha256:d5d486b97f1c45d4ef0a4d24429fbc581b2d69c5a33a75e97c57a833505c1cd9

Observation ba2b3e59-49c8-4717-a51d-4611be2de801 · outbound

This paper cites Revisiting over-smoothing and over-squashing using ollivier- ricci curvature,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Revisiting over-smoothing and over-squashing using ollivier- ricci curvature,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:01.615992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:46.480537Z digest=sha256:2708c5490a941efc297a569650c169e911fa4d1c5add565a52836c6eff757b2d

Observation be5677ac-0787-42ae-9ce7-eba4f7a62a3b · outbound

This paper cites Understanding over- squashing in gnns through the lens of effective resistance,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Understanding over- squashing in gnns through the lens of effective resistance,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:01.364642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:46.674757Z digest=sha256:95cecf51d5160c735f2574d381c98fbd9c13ebdb7b67966466bc9c27fb9c5301

Observation b880ff2d-faf3-4bfb-ac3d-c1040911d743 · outbound

This paper cites Dif- fWire: Inductive Graph Rewiring via the Lovász Bound,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Dif- fWire: Inductive Graph Rewiring via the Lovász Bound,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:01.044979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:46.812848Z digest=sha256:4d713ebb6c9a8c2e3454302b39445fabd9ddc359cc4a5720ac6883ac092f0d41

Observation c2ce5104-11b2-46c2-a133-9372ab12e6e6 · outbound

This paper cites Rewiring with po- sitional encodings for graph neural networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Rewiring with po- sitional encodings for graph neural networks,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:00.756060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:46.964989Z digest=sha256:40c76d359024397654d5ff624334b79186c12a06cf9c91d76d643da247ffc44c

Observation 52cccd02-cf47-4d0c-bdd3-5f5677b68772 · outbound

This paper cites Locality-aware graph rewiring in GNNs,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Locality-aware graph rewiring in GNNs,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:00.444823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:47.174992Z digest=sha256:3b35295bde493d4ae7da6aa7b5c515c003f24efc491a1cfa50ff549ad890e69c

Observation 63b450d4-a828-454f-9f69-0efa1d3ccf53 · outbound

This paper cites On over-squashing in message passing neural networks: the impact of width, depth, and topology,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks On over-squashing in message passing neural networks: the impact of width, depth, and topology,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:03:00.169118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:47.328324Z digest=sha256:149e5ba9f1716d542a02d8676d99a762d19418a406a2dd6e8cc36886894c1a0e

Observation 503044c2-b5bd-44a6-86e2-5643f9a0c27d · outbound

This paper cites Panda: expanded width-aware message passing beyond rewiring,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Panda: expanded width-aware message passing beyond rewiring,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:59.923179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:47.552082Z digest=sha256:9e771afe5426da044988eb65b60162555588253126440c17245b895061a1e0b1

Observation 8c11495f-3233-4d65-9707-a3669ce1262d · outbound

This paper cites How does over-squashing affect the power of GNNs?.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks How does over-squashing affect the power of GNNs?

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:59.514910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:47.741885Z digest=sha256:bc7183ac8a046d872c05a155257f5e8e988d8ce68200e50407b8d1ed3a86e9d0

Observation 9991c168-b362-4dc2-a0cc-c4180bb59265 · outbound

This paper cites Oversquashing in gnns through the lens of information contraction and graph expansion,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Oversquashing in gnns through the lens of information contraction and graph expansion,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:59.283629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:47.930476Z digest=sha256:10d9d144ef1975e761d5da242a32575d0989e7ed925822fca7bb5a38a9c389e6

Observation 9a48eafa-a6c5-45ca-a7b8-7ada873b91e4 · outbound

This paper cites Graph rewiring and preprocessing for graph neural networks based on effective resistance,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Graph rewiring and preprocessing for graph neural networks based on effective resistance,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:59.057682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:48.097505Z digest=sha256:5665295b2cfd37d64d6eb40aded3498d628dd763f68d52fd292e64a7ce969791

Observation 11ebf6e9-40e3-4ed7-aaed-c35b20769e94 · outbound

This paper cites Diffusion improves graph learning,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Diffusion improves graph learning,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:58.864856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:48.345816Z digest=sha256:88368665c9ba559e43db30364d09746864c17206835680e39cbb57450b1a3a38

Observation 7498984c-3ed9-4f41-9913-1ad0859dd1c8 · outbound

This paper cites Probabilistically rewired message-passing neural networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Probabilistically rewired message-passing neural networks,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:58.581683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:48.483652Z digest=sha256:cd626b22ba4adad17bf9dcbe8f42cf39dc23aa131683a9f197ead1675c27bc2d

Observation 9d425f2e-1ecb-4b68-80fe-a206591f9bf4 · outbound

This paper cites Expander graph propagation,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Expander graph propagation,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:58.347444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:48.608296Z digest=sha256:398d29a3cce6a5aca0a9c99048dc44cdd342edb39deaedcb7fee0bb0dd4e0dee

Observation b4cf81a2-f8e4-4a87-8884-e90576a2e757 · outbound

This paper cites Cayley graph propagation,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Cayley graph propagation,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:58.192538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:48.734757Z digest=sha256:0221a7c682971759032a5b3a8d38205efc6334eee5f9a1062b14135033433559

Observation 518c10d5-cb3c-456a-afbb-cbea9e44b317 · outbound

This paper cites Delaunay graph: Addressing over-squashing and over-smoothing using delaunay triangulation,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Delaunay graph: Addressing over-squashing and over-smoothing using delaunay triangulation,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:57.908617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:48.854841Z digest=sha256:32413f6fbde8b697e5cbe2210001381f57defd87977b8edf19844d16e9a6208c

Observation 683cf230-5627-497a-beaa-276def3258a9 · outbound

This paper cites On the connection between mpnn and graph transformer,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks On the connection between mpnn and graph transformer,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:57.734825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:48.966466Z digest=sha256:e6318deb0e009f2ceb6b342cb048cf4825014142d50177d5c4805135e7881567

Observation d264743a-11e9-4fe7-b3bf-a637b3b2372d · outbound

This paper cites Un- derstanding virtual nodes: Oversquashing and node heterogeneity,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Un- derstanding virtual nodes: Oversquashing and node heterogeneity,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:57.513395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:49.116188Z digest=sha256:c85baee6c7332fcfdd703025ec5454bae3a71206be2ddbfb1ca4701fe69fa114

Observation abe6a80d-bb01-4ce9-ad9b-d996545a91a8 · outbound

This paper cites An analysis of virtual nodes in graph neural networks for link prediction,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks An analysis of virtual nodes in graph neural networks for link prediction,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:57.242534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:49.286371Z digest=sha256:3ed0bb87ee6eb13b3ddff6d00e3b1d99bc5d60d2e31d1e120fc8888a45714205

Observation 61556ef5-3180-450f-bf59-b87f29b350d1 · outbound

This paper cites VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-05T15:02:49.484920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:02:49.484920Z digest=sha256:73847e3f4fb25fa362f1a58a8affd5d1772b17fc50459f8c0bab43d331600974

Observation 16a40f70-0282-44ef-aec5-8a77e385e28c · outbound

This paper cites Improving equivariant graph neural networks on large geometric graphs via virtual nodes learning,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Improving equivariant graph neural networks on large geometric graphs via virtual nodes learning,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:57.074822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:49.676602Z digest=sha256:22317c5716128d054232c2a345ed00dbf0a7df6867a298caab7595210fb57f67

Observation 8695b6e8-e544-4953-829d-07d9c7e45268 · outbound

This paper cites E(n) equivariant graph neural networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks E(n) equivariant graph neural networks,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:56.818374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:49.804749Z digest=sha256:a201c9db8f09e743946958dc2aa98707c8485c4939c5b05d186abbab49ec1ede

Observation b84bf950-2cd2-4827-96de-e978ad41d0bb · outbound

This paper cites Probabilistic graph rewiring via virtual nodes,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Probabilistic graph rewiring via virtual nodes,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:56.618527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:49.933136Z digest=sha256:1ae712a8618b06552d9f897a3ffaf36511cdae03c60d44dcbb9c015054a582af

Observation 2ce360ab-28b1-4a7e-a32c-dff637fc21a2 · outbound

This paper cites A faster algorithm for betweenness centrality*,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks A faster algorithm for betweenness centrality*,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:56.324752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:50.067983Z digest=sha256:b87c07c34e81d77e85c841db9c9975a5e0e17c0e895a0b48e17d35312ee07aca

Observation ba5f7440-5e58-42bc-ab16-e5aa62cba7ff · outbound

This paper cites Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:55.974822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:50.186069Z digest=sha256:37b86f7aa16cdb22a7e1b3c554b473d2627e26273636b35e061fcd54f3d30deb

Observation 8a751c50-52c8-4c2c-b6e0-e216c635c71f · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Semi-supervised classification with graph convolutional networks,

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T15:02:50.308320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:02:50.308320Z digest=sha256:b8366ca64f059bbe1f523ac11eace09f4aaf5d9574a390ddba777f9e70bcf78f

Observation 4c96518a-98d1-4cb3-9962-0436b4f6c7ce · outbound

This paper cites How powerful are graph neural networks?.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks How powerful are graph neural networks?

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-05T15:02:50.487659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:02:50.487659Z digest=sha256:4a64a5ddf2e4fa72c6af537ed44e8efa8d91072a79ff62db108c00ae637a54c0

Observation 04263f78-18a4-4791-ac29-8f5c4b80ad02 · outbound

This paper cites Graph attention networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Graph attention networks,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:55.498558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:50.637582Z digest=sha256:5ecb57f697c433e16d764b3fb8de47e5a1e2e80235fcd85b41756a78f0997d50

Observation 759fcd1a-9c0c-4f0d-9543-095d71e1d49f · outbound

This paper cites The pagerank citation ranking: Bringing order to the web.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks The pagerank citation ranking: Bringing order to the web

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:55.293960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:50.806968Z digest=sha256:89260d0b09cd537202975326cfc2cfe4706c0c0a70c28e2524fe4db016850114

Observation 9748ab18-d8ba-4301-886b-7b1bf11b08ad · outbound

This paper cites Near linear time algorithm to detect community structures in large-scale networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Near linear time algorithm to detect community structures in large-scale networks,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:55.043165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:51.075877Z digest=sha256:9a85a97fe284b51647ad182d92b49cf54b870e6efe86d046bdd2b855d3960dca

Observation 63bf7b19-e573-47eb-8a9a-b6bc0b1dfac5 · outbound

This paper cites Attention is all you need,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Attention is all you need,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:54.826239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:51.233192Z digest=sha256:31dfb789d3cfaa8c6b5557b8c82f698f3965e0be8e63e1c13eddb9036b45abcc

Observation cc495b78-da42-491a-9078-7d28d23d18cb · outbound

This paper cites Tudataset: A collection of benchmark datasets for learning with graphs,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Tudataset: A collection of benchmark datasets for learning with graphs,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:54.504961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:51.358958Z digest=sha256:b1beddac3aaa6f5ae68a3a4387dae0a2b8ccb4f2feb67d58e8afd11040f7c7e8

Observation 55e26a53-5b03-40e2-86f0-fb2d691ed2ba · outbound

This paper cites Geom-gcn: Geometric graph convolutional networks,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Geom-gcn: Geometric graph convolutional networks,

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-05T15:02:51.495995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:02:51.495995Z digest=sha256:3a172d32787446931d743e213bfba4610f4fdbcfc9b64b59fceb86663c27c2da

Observation e5a348c0-388c-489c-b563-821ed568bb71 · outbound

This paper cites Collective classification in network data,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Collective classification in network data,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:54.125674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:51.599487Z digest=sha256:7f88b1183faa8d423af1c08ec1dfb52d1f5803d34e4e3455f9ab5ba666a7eaa9

Observation d0d0c860-a361-4d08-985e-6c21276c56b7 · outbound

This paper cites Adam: A method for stochastic optimization,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Adam: A method for stochastic optimization,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:53.679485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:51.824911Z digest=sha256:665266ed30649c0377d9ef27f9132f311c2e830cd8f1d74f1cbe90d6ba37697a

Observation 848ceffe-3b9a-48d0-b97b-e7c2ecfc2128 · outbound

This paper cites Batch normalization: accelerating deep net- work training by reducing internal covariate shift,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Batch normalization: accelerating deep net- work training by reducing internal covariate shift,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:53.244382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:51.954749Z digest=sha256:27e1d3ee17ef53d4c116ce00cc51eb24afc89b3b5114463f1aa8c82181534e65

Observation 21c3ccfa-a961-4dcc-ae41-ece0853004f3 · outbound

This paper cites Minimizing effective resistance of a graph,.

Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Minimizing effective resistance of a graph,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:02:52.894934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-05T15:02:52.070817Z digest=sha256:9615b0282b8eefdfed4f936a71cca3edc0efba936fb6b4c3362e7eae768e5765

Pith citing papers

No inbound Pith citation observations are available.