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Paper Citation Record · LEDGER

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs

As of 9 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2506.05039.

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

pith.paper-citation-record.v1
2506.05039 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:36:54.653442Z

measured 51 of 51 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 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

51 of 51 outbound references displayed

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  • verified fuzzy25
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7e3afb58-fd58-4cea-8906-3f0fabc3b7c9 · outbound

This paper cites write newline.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs write newline

Reference 1

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Observation 55d08321-fbe6-4f2c-b59c-5d76d6bd6518 · outbound

This paper cites V., and Galstyan, A.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs V., and Galstyan, A

Reference 2

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verified fuzzy
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This paper cites an unresolved cited work.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Unresolved cited work

Reference 3

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Observation 2a899830-c526-4500-81bc-fad05a0d8836 · outbound

This paper cites Make heterophilic graphs better fit GNN: A graph rewiring approach.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Make heterophilic graphs better fit GNN: A graph rewiring approach

Reference 4

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Observation 8cb51053-aa35-4853-a711-d424033dc889 · outbound

This paper cites Translating embeddings for modeling multi-relational data.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Translating embeddings for modeling multi-relational data

Reference 5

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Observation 5e7680aa-cc64-4ea8-af53-2da082035804 · outbound

This paper cites Simple and deep graph convolutional networks.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Simple and deep graph convolutional networks

Reference 6

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Observation 9da03bab-5cfe-40b4-9043-af45a9bd81b5 · outbound

This paper cites Fede: Embedding knowledge graphs in federated setting.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Fede: Embedding knowledge graphs in federated setting

Reference 7

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Source-reported events for the cited work

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Observation dad8ff0e-700a-449b-bca4-c7deaab9c30d · outbound

This paper cites Refactor gnns: Revisiting factorisation-based models from a message-passing perspective.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Refactor gnns: Revisiting factorisation-based models from a message-passing perspective

Reference 8

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Source-reported events for the cited work

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Observation f035741f-371a-4208-ba98-f2e497930a10 · outbound

This paper cites Adaptive universal generalized pagerank graph neural network.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Adaptive universal generalized pagerank graph neural network

Reference 9

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Source-reported events for the cited work

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Observation 4ee7d64b-ee4b-4239-9de8-e82c52c48efc · outbound

This paper cites Learning structural node embeddings via diffusion wavelets.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Learning structural node embeddings via diffusion wavelets

Reference 10

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Observation 09ae9146-2c4e-4d1b-bc90-5b5b2b574031 · outbound

This paper cites and Leskovec, J.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs and Leskovec, J

Reference 11

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Observation 8917ce91-6e72-4259-a6c7-07a252ccc9a4 · outbound

This paper cites an unresolved cited work.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Unresolved cited work

Reference 12

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Source-reported events for the cited work

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Observation de1c4691-6deb-4d42-b803-38b5b776aba2 · outbound

This paper cites L., Ying, Z., and Leskovec, J.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs L., Ying, Z., and Leskovec, J

Reference 13

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verified fuzzy
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Source-reported events for the cited work

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Observation dd6b1f81-874c-4d0e-a23a-3adcf053a2d7 · outbound

This paper cites Graph-MLP: Node Classification without Message Passing in Graph.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Graph-MLP: Node Classification without Message Passing in Graph

Reference 14

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Observation 069d7bf3-2cf3-4275-bdff-d571d404f78e · outbound

This paper cites and Benson, A.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs and Benson, A

Reference 15

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Observation dbcbe1c3-37a6-4a20-b068-0590c01cb7c0 · outbound

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iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Unresolved cited work

Reference 16

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Observation 06edfb58-07e6-4e40-bc80-99abee0b6640 · outbound

This paper cites Edge-splitting MLP : Node classification on homophilic and heterophilic graphs without message passing.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Edge-splitting MLP : Node classification on homophilic and heterophilic graphs without message passing

Reference 17

Resolution
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Observation 7e5bba33-dc99-4223-ae8a-da3a1de78457 · outbound

This paper cites Canonical tensor decomposition for knowledge base completion.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Canonical tensor decomposition for knowledge base completion

Reference 18

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Observation 9cd00720-2520-4ebc-8383-7fe8fae43cb5 · outbound

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iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Unresolved cited work

Reference 19

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Observation 1bb15937-1bee-4555-a1a8-9cb4fb2ff5c9 · outbound

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iN2V: Bringing Transductive Node Embeddings to Inductive Graphs and Scherp, A

Reference 20

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Observation d468cc02-0522-4b80-b100-2adf57722574 · outbound

This paper cites L., Gupta, V., Bhalerao, O., and Lim, S.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs L., Gupta, V., Bhalerao, O., and Lim, S

Reference 21

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Source-reported events for the cited work

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This paper cites Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities

Reference 22

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Observation 3ab7ea8c-2c74-42b9-b2d9-4b4771716e0a · outbound

This paper cites Revisiting heterophily for graph neural networks.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Revisiting heterophily for graph neural networks

Reference 23

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Observation 3886432c-2490-4fcc-877b-2f31a5b29601 · outbound

This paper cites Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Reference 24

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Observation 9847853d-67d5-4964-b985-5f39febd06bc · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Efficient Estimation of Word Representations in Vector Space

Reference 25

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Observation 181faf5f-ba06-48ee-a535-46309d8f37cf · outbound

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iN2V: Bringing Transductive Node Embeddings to Inductive Graphs and Prokhorenkova, L

Reference 26

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Source-reported events for the cited work

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iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Query-driven active surveying for collective classification

Reference 27

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Observation c2aa9301-1e79-4147-a164-a5a689ae99d3 · outbound

This paper cites subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs

Reference 28

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Observation 6d88f18e-c33f-419e-a061-4fa131552d1a · outbound

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iN2V: Bringing Transductive Node Embeddings to Inductive Graphs graph2vec: Learning Distributed Representations of Graphs

Reference 29

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Observation 53d46ba2-78c8-4773-b6e6-a75af69586c4 · outbound

This paper cites C., Lei, Y., and Yang, B.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs C., Lei, Y., and Yang, B

Reference 30

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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.

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Observation c12efb71-a42d-439f-82ef-b1342c7b4acc · outbound

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iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Deepwalk: online learning of social representations

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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This paper cites Characterizing graph datasets for node classification: Homophily-heterophily dichotomy and beyond.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Characterizing graph datasets for node classification: Homophily-heterophily dichotomy and beyond

Reference 32

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verified fuzzy
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Source-reported events for the cited work

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

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Observation a76cfe1f-a668-403c-b310-a7b1cc0fa08f · outbound

This paper cites A critical look at the evaluation of gnns under heterophily: Are we really making progress? In ICLR 2023.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs A critical look at the evaluation of gnns under heterophily: Are we really making progress? In ICLR 2023

Reference 33

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

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Observation 4a7a7340-9c35-46bf-8947-0650595a88c2 · outbound

This paper cites How Does Knowledge Evolve in Open Knowledge Graphs? Transactions on Graph Data and Knowledge, 1 0 (1): 0 11:1--11:59, 2023.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs How Does Knowledge Evolve in Open Knowledge Graphs? Transactions on Graph Data and Knowledge, 1 0 (1): 0 11:1--11:59, 2023

Reference 34

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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.

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Observation c1bf3c60-289d-4632-865a-7ac85363c8aa · outbound

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iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Unresolved cited work

Reference 35

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Source-reported events for the cited work

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Observation 93720469-590f-403e-be1f-da13637725a9 · outbound

This paper cites o tzsch, M., L \' e cu \' e , F., Fl \.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs o tzsch, M., L \' e cu \' e , F., Fl \

Reference 36

Resolution
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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.

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Observation 11f80065-2f4b-47ae-8a81-760126936898 · outbound

This paper cites I., Chamberlain, B.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs I., Chamberlain, B

Reference 37

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation 93a7b58b-4fbb-4b0d-ab2a-da7cd448c9a2 · outbound

This paper cites F., and Catalyurek, U.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs F., and Catalyurek, U

Reference 38

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation 9c00cdbb-2f95-4511-9990-1991d6550381 · outbound

This paper cites Collective classification in network data.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Collective classification in network data

Reference 39

Resolution
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Source-reported events for the cited work

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Observation e0eac5ae-12c4-4021-a72a-305aa98fa1f1 · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Pitfalls of Graph Neural Network Evaluation

Reference 40

Resolution
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Source-reported events for the cited work

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Observation 92596f72-cc3b-4458-8c3b-2c93b6eaed0c · outbound

This paper cites Rotate: Knowledge graph embedding by relational rotation in complex space.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Rotate: Knowledge graph embedding by relational rotation in complex space

Reference 41

Resolution
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Source-reported events for the cited work

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

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Observation 8f76f3cb-7673-445b-9edc-8a2fa999d62a · outbound

This paper cites LINE: large-scale information network embedding.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs LINE: large-scale information network embedding

Reference 42

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 336bb8f6-9b9d-47a0-9f8a-cae4f2182369 · outbound

This paper cites an unresolved cited work.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Unresolved cited work

Reference 43

Resolution
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Source-reported events for the cited work

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Observation 1bf98797-99be-4d20-a9e5-2802124334f5 · outbound

This paper cites Complex embeddings for simple link prediction.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Complex embeddings for simple link prediction

Reference 44

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation 1a254296-d459-4ae6-bcaf-e19dc7825574 · outbound

This paper cites Graph attention networks.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Graph attention networks

Reference 45

Resolution
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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.

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Observation dd55959d-b734-4444-8f94-f0a49947dafb · outbound

This paper cites Knowledge graph embedding by translating on hyperplanes.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Knowledge graph embedding by translating on hyperplanes

Reference 46

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6d31011e-eac5-4502-89f1-f0e17de93529 · outbound

This paper cites an unresolved cited work.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Unresolved cited work

Reference 47

Resolution
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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.

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Observation 6931c187-7b5c-428c-b514-38c19a3a7a04 · outbound

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

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Representation learning on graphs with jumping knowledge networks

Reference 48

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation 305cdcad-2dc6-4f6d-9bf1-bf91e745d271 · outbound

This paper cites How powerful are graph neural networks? In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs How powerful are graph neural networks? In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019

Reference 49

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation 263005cf-8404-44dd-ad4c-5a42cf625862 · outbound

This paper cites Graph-less neural networks: Teaching old mlps new tricks via distillation.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Graph-less neural networks: Teaching old mlps new tricks via distillation

Reference 50

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation 0b9e1655-151f-41dc-8ff9-d1285fa32e05 · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 51

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Pith citing papers

No inbound Pith citation observations are available.