Paper Citation Record · LEDGER

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization

As of 21 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2506.14114.

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

pith.paper-citation-record.v1
2506.14114 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:26:20.955313Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

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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

52 of 52 outbound references displayed

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verified fuzzy1
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External citation measurements

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Outbound references

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

Reference 1

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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This paper cites Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models.

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models

Reference 7

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization OLGA: One-cLass Graph Autoencoder

Reference 10

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

Reference 11

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

Reference 12

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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This paper cites ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities.

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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This paper cites LocalGCL: Local-aware Contrastive Learning for Graphs.

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization LocalGCL: Local-aware Contrastive Learning for Graphs

Reference 16

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Observation c44ce8d4-aa4e-4ee4-ae2a-a8b7bc04b415 · outbound

This paper cites Revisiting Random Walks for Learning on Graphs.

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Revisiting Random Walks for Learning on Graphs

Reference 19

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Semi-Supervised Classification with Graph Convolutional Networks

Reference 20

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Variational Graph Auto-Encoders

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Gribova, Vladimir Fedorovich Filaretov, and De-Shuang Huang

Reference 24

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Graph Positional Autoencoders as Self-supervised Learners

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Reconsidering the Performance of GAE in Link Prediction

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Poincar\'e Wasserstein Autoencoder

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unsupervised Embedding Quality Evaluation

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Graph Clustering with Graph Neural Networks

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Deep Graph Infomax

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Variational Graph Contrastive Learning

Reference 41

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization How Powerful are Graph Neural Networks?

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning

Reference 43

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Structure-Preference Enabled Graph Embedding Generation under Differential Privacy

Reference 48

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Structure-Preference Enabled Graph Embedding Generation under Differential Privacy

Reference 49

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

Reference 50

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Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Unresolved cited work

Reference 52

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Observation 6f52ff62-3ec2-4bc0-b756-5d5466b33e04 · outbound

This paper cites Deep Graph Contrastive Representation Learning.

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Deep Graph Contrastive Representation Learning

Reference 53

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Pith citing papers

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