GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction | Paper Citation Record

Observation 95d24edb-a2f5-48e6-94f2-aa871485e4e0 · outbound

This paper cites Friends and neighbors on the web.Social Networks, 25(3): 211–230, 2003.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Friends and neighbors on the web.Social Networks, 25(3): 211–230, 2003

Reference 1

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Observation 31ebb34f-efc8-46dd-80a4-5e587bdadeca · outbound

This paper cites Simple Path Structural Encoding for Graph Transformers.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Simple Path Structural Encoding for Graph Transformers

Reference 2

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Observation 44c28062-baa2-4eee-b187-fa4cb82f86d6 · outbound

This paper cites An optimal lower bound on the number of variables for graph identification.Combinatorica, 12(4):389–410, 1992.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction An optimal lower bound on the number of variables for graph identification.Combinatorica, 12(4):389–410, 1992

Reference 3

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Observation fd3e89bc-45e0-4d4b-b2f1-3e38bea35546 · outbound

This paper cites Bronstein, and Max Hansmire.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Bronstein, and Max Hansmire

Reference 4

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Observation a2ed9a2e-4c88-4856-9502-fdbb28f79f25 · outbound

This paper cites Edge classification on graphs: New directions in topological imbalance.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Edge classification on graphs: New directions in topological imbalance

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:42.469598Z

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Observation 3e8fb04e-ce2f-47df-9e6c-82ec2b5ff28e · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 6

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Observation 7cb046cb-02db-456b-8449-e4d9aef30a5a · outbound

This paper cites Cormen, Charles E.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Cormen, Charles E

Reference 7

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Observation 84b6e30c-b9ce-4bda-8a6f-588f3c12e9f4 · outbound

This paper cites Generalizations of k-dimensional weisfeiler–leman stabiliza- tion.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Generalizations of k-dimensional weisfeiler–leman stabiliza- tion

Reference 8

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Observation 84bf3830-db83-4db4-afe2-8ecd8196f958 · outbound

This paper cites Thelinkregressionproblemingraphstreams.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Thelinkregressionproblemingraphstreams

Reference 9

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Observation 782672af-7b51-4830-8c9d-3b3603115d06 · outbound

This paper cites node2vec: Scalable feature learning for networks.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction node2vec: Scalable feature learning for networks

Reference 10

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Observation a5c6c88b-12ee-4b87-a581-52207908f683 · outbound

This paper cites Exploring network structure, dynamics, and function using networkx.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Exploring network structure, dynamics, and function using networkx

Reference 11

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Observation ecc48b01-91eb-431c-af2d-53bf9da9cad4 · outbound

This paper cites Inductive representation learning on large graphs.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Inductive representation learning on large graphs

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:41.815395Z

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Observation eec8ca77-ed7c-4a4c-8ba5-41d8822504d3 · outbound

This paper cites Inductive representation learning on large graphs.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Inductive representation learning on large graphs

Reference 13

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Observation 0ca2ca38-f79b-4570-994c-73c8421b3fd9 · outbound

This paper cites Long short-term memory.Neural computation, 9(8): 1735–1780, 1997.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Long short-term memory.Neural computation, 9(8): 1735–1780, 1997

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:15:33.879537Z

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Observation 08ff7a8b-10e3-478e-a946-749b1d02a62c · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Open graph benchmark: Datasets for machine learning on graphs

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:41.516720Z

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Observation 5ad7ba7d-47f9-46ee-9086-2701f25f9f19 · outbound

This paper cites Prediction of protein–protein interaction using graph neural networks.Scientific Reports, 12(1):8360, 2022.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Prediction of protein–protein interaction using graph neural networks.Scientific Reports, 12(1):8360, 2022

Reference 16

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Observation cf6cfd9b-d1c5-464a-adcc-39c47ddd4af4 · outbound

This paper cites A new status index derived from sociometric analysis.Psychometrika, 18(1):39–43, 1953.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction A new status index derived from sociometric analysis.Psychometrika, 18(1):39–43, 1953

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T19:15:34.140884Z

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Observation 315bbbae-4424-4d42-b903-658c5f0a4e17 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Semi-Supervised Classification with Graph Convolutional Networks

Reference 18

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Observation a088b8e8-366b-4e32-8318-d15a761dae6e · outbound

This paper cites Variational Graph Auto-Encoders.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Variational Graph Auto-Encoders

Reference 19

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Observation 1d2eafb1-0634-411f-a1dc-a0c463bd5980 · outbound

This paper cites Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:41.354086Z

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Observation 06405f63-729c-4e2d-8a55-8024e4086864 · outbound

This paper cites Distance encoding: Design provably more powerful neural networks for graph representation learning.Advances in Neural Information Processing Systems, 33:4465–4478, 2020.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Distance encoding: Design provably more powerful neural networks for graph representation learning.Advances in Neural Information Processing Systems, 33:4465–4478, 2020

Reference 21

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Observation f784b1df-dc8a-4849-9167-462d78f03a64 · outbound

This paper cites Line graph neural networks for link weight prediction.Physica A: Statistical Mechanics and its Applications, page 130406, 2025.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Line graph neural networks for link weight prediction.Physica A: Statistical Mechanics and its Applications, page 130406, 2025

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:40.910965Z

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Observation a5466ac8-77e0-44a0-a845-a2e140b0abfa · outbound

This paper cites The link prediction problem for social net- works.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction The link prediction problem for social net- works

Reference 23

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Observation fea567eb-8d40-4287-a321-2c19e2853fe8 · outbound

This paper cites Computational complexity of the weisfeiler-leman dimension.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Computational complexity of the weisfeiler-leman dimension

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:40.612804Z

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Observation 7ab41edb-031a-4167-96ad-903d9abd258b · outbound

This paper cites Linkpredictionincomplexnetworks: Asurvey.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Linkpredictionincomplexnetworks: Asurvey

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:40.418763Z

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Observation 00c9e8a0-baeb-49a7-a47c-a47886683633 · outbound

This paper cites Link prediction via matrix factorization.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Link prediction via matrix factorization

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:40.157871Z

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Observation 29b0d07e-7d13-4cc2-bf44-7ef7ad6f09c2 · outbound

This paper cites Weisfeiler and leman go neural: Higher-order graph neural networks.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Weisfeiler and leman go neural: Higher-order graph neural networks

Reference 27

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Observation 3dcb9246-b19e-4501-b269-a00292e085d1 · outbound

This paper cites Clusteringandpreferentialattachmentingrowingnetworks.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Clusteringandpreferentialattachmentingrowingnetworks

Reference 28

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Observation a7017222-74c1-430c-b3ea-37db47b87a7e · outbound

This paper cites A review of relational machine learning for knowledge graphs.Proceedings of the IEEE, 104(1):11–33, 2015.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction A review of relational machine learning for knowledge graphs.Proceedings of the IEEE, 104(1):11–33, 2015

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Observation 5ec297ae-94f8-44d4-b89f-df264212e814 · outbound

This paper cites Knowledge graph embedding for link prediction: A comparative analysis.ACM Transactions on Knowledge Discovery from Data (TKDD), 15(2):1–49, 2021.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Knowledge graph embedding for link prediction: A comparative analysis.ACM Transactions on Knowledge Discovery from Data (TKDD), 15(2):1–49, 2021

Reference 30

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Observation f86027f0-500f-48e0-b7a4-30b011fad79f · outbound

This paper cites On the Equivalence between Positional Node Embeddings and Structural Graph Representations.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction On the Equivalence between Positional Node Embeddings and Structural Graph Representations

Reference 31

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Observation b4cfa627-b43c-4d1d-b11d-39c3745afe7f · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 32

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Observation 98f4f4d0-6865-4957-8a50-82da9c889ecf · outbound

This paper cites Graph attention networks.stat, 1050(20):10–48550, 2017.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Graph attention networks.stat, 1050(20):10–48550, 2017

Reference 33

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Observation ab9f108a-b015-4208-932b-fbf6b1c8e13a · outbound

This paper cites Graph attention networks.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Graph attention networks

Reference 34

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Observation d02edf43-5ebb-496a-b2d6-30d20ccd9606 · outbound

This paper cites Equivariant and stable positional encoding for more powerful graph neural networks.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Equivariant and stable positional encoding for more powerful graph neural networks

Reference 35

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Observation 3d0f6af5-58c3-4b82-87b6-c407c3698ad8 · outbound

This paper cites Neural common neighbor with completion for link prediction.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Neural common neighbor with completion for link prediction

Reference 36

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Observation 762eb34e-e48f-44a1-9405-374dd042e1a5 · outbound

This paper cites Apan: Asynchronous propagation attention network for real-time temporal graph embedding.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Apan: Asynchronous propagation attention network for real-time temporal graph embedding

Reference 37

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Observation 5f744f58-7955-41f0-a497-a26a426271a7 · outbound

This paper cites How powerful are graph neural networks?, 2019.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction How powerful are graph neural networks?, 2019

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This paper cites Revisiting semi-supervised learning with graph embeddings.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Revisiting semi-supervised learning with graph embeddings

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Observation adaefc00-b2c6-4f60-abec-64ea44158356 · outbound

This paper cites Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Do transformers really perform badly for graph representation?Advances in neural information processing systems, 34:28877–28888, 2021

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Observation 9e39f895-32d1-4e6c-8203-ad3a40b7c3c9 · outbound

This paper cites Graph convolutional neural networks for web-scale recommender systems.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Graph convolutional neural networks for web-scale recommender systems

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:38.199404Z

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Observation e4397669-eb2b-4788-b79e-3e4a21b8aaf0 · outbound

This paper cites Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction.Advances in Neural Information Processing Systems, 34:13683–13694, 2021.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction.Advances in Neural Information Processing Systems, 34:13683–13694, 2021

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Observation a634f899-90f4-4023-8aba-745713255f21 · outbound

This paper cites Link prediction based on graph neural networks.Advances in neural information processing systems, 31, 2018.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Link prediction based on graph neural networks.Advances in neural information processing systems, 31, 2018

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Observation 1bd891e7-5e39-4ef1-9fb4-b40b6de78138 · outbound

This paper cites Labelingtrick: Atheoryofusing graph neural networks for multi-node representation learning.Advances in Neural Information Processing Systems, 34:9061–9073, 2021.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Labelingtrick: Atheoryofusing graph neural networks for multi-node representation learning.Advances in Neural Information Processing Systems, 34:9061–9073, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:37.657678Z

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Observation 82f238f1-0e54-4457-bf58-a1707f158780 · outbound

This paper cites Progresses and challenges in link prediction.Iscience, 24(11), 2021.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Progresses and challenges in link prediction.Iscience, 24(11), 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:15:37.490374Z

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Observation 8b1db422-8be2-48fa-9af7-06a0fc2e9044 · outbound

This paper cites Predicting missing links via local information.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Predicting missing links via local information

Reference 46

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Observation 68867ffe-812a-434e-a398-aa96fea3bd70 · outbound

This paper cites Neural bellman-ford networks: A general graph neural network framework for link prediction.Advances in neural information processing systems, 34:29476–29490, 2021.

GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Neural bellman-ford networks: A general graph neural network framework for link prediction.Advances in neural information processing systems, 34:29476–29490, 2021

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GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction Unresolved cited work

Reference 2018

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