Paper Citation Record · LEDGER

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs

As of 22 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

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measured 51 of 51 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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

Reference 3

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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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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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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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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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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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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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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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This paper cites and Leskovec, J.

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

Reference 11

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Reference 12

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

Reference 15

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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

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

Reference 20

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

Reference 21

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

Reference 25

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

Reference 26

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

Reference 29

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

Reference 30

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

Reference 31

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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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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

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

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

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

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

Reference 39

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

Reference 40

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

Reference 41

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

Reference 42

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

Reference 44

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

Reference 45

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

Reference 46

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Reference 47

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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

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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; raw_fallback, observed 2026-08-07T10:36:55.611738Z

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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; raw_fallback, observed 2026-08-07T10:36:55.596077Z

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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; raw_fallback, observed 2026-08-07T10:36:55.580784Z

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Pith citing papers

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