Paper Citation Record · LEDGER

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps

As of 12 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.15177.

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

pith.paper-citation-record.v1
2505.15177 v2

Coverage vector

measured 47 of 47 reference resolution

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measured 47 of 47 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 2b45e70a-7560-4c02-a3b8-4243a1c3c91b · outbound

This paper cites Spectral graph theory.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Spectral graph theory

Reference 1

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Observation 2b4ae3b5-2613-4501-a93f-91552a197e82 · outbound

This paper cites the correct side.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps the correct side

Reference 3

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Observation 0889daab-16fc-404d-beb2-027f25ccaf44 · outbound

This paper cites Expander graphs and their applications.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Expander graphs and their applications

Reference 7

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This paper cites A comprehensive survey on deep graph representation learning.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps A comprehensive survey on deep graph representation learning

Reference 11

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

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Semi-Supervised Classification with Graph Convolutional Networks

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This paper cites Rethinking graph transformers with spectral atten- tion.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Rethinking graph transformers with spectral atten- tion

Reference 13

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Observation f38e36eb-bc6c-4a3f-927a-abc97a2fd352 · outbound

This paper cites Good-d: On unsupervised graph out-of- distribution detection.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Good-d: On unsupervised graph out-of- distribution detection

Reference 17

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Observation a3032a17-5c52-4989-bb12-44c6ebabfebf · outbound

This paper cites Towards self- interpretable graph-level anomaly detection.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Towards self- interpretable graph-level anomaly detection

Reference 18

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Observation 9e84f9e9-cd99-4e2a-8253-bb291181e08f · outbound

This paper cites Deep graph-level anomaly de- tection by glocal knowledge distillation.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Deep graph-level anomaly de- tection by glocal knowledge distillation

Reference 19

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Observation 94d5a882-766e-4e80-b679-7556b52b14ba · outbound

This paper cites Towards graph-level anomaly detection via deep evolutionary mapping.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Towards graph-level anomaly detection via deep evolutionary mapping

Reference 20

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Observation deeeb3b6-3cdc-4397-be57-5792fb367b05 · outbound

This paper cites Provably powerful graph networks.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Provably powerful graph networks

Reference 21

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Observation 7c6c03c8-7764-407a-8908-3c81b6c9681f · outbound

This paper cites GraphiT: Encoding Graph Structure in Transformers.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps GraphiT: Encoding Graph Structure in Transformers

Reference 22

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This paper cites A new method to predict anomaly in brain network based on graph deep learning.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps A new method to predict anomaly in brain network based on graph deep learning

Reference 23

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

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps TUDataset: A collection of benchmark datasets for learning with graphs

Reference 24

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Observation 21a88b25-5c34-448c-b601-80c84f1faeee · outbound

This paper cites Semi-supervised domain adaptation in graph transfer learning.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Semi-supervised domain adaptation in graph transfer learning

Reference 25

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This paper cites Information filtering and in- terpolating for semi-supervised graph domain adaptation.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Information filtering and in- terpolating for semi-supervised graph domain adaptation

Reference 26

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Observation ef9dde21-b767-498c-9ecf-a5e19ec93a3e · outbound

This paper cites Towards contin- uous reuse of graph models via holistic memory diversi- fication.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Towards contin- uous reuse of graph models via holistic memory diversi- fication

Reference 27

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This paper cites Optimizing ood detection in molecular graphs: A novel approach with diffusion mod- els.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Optimizing ood detection in molecular graphs: A novel approach with diffusion mod- els

Reference 29

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This paper cites Rankfeat: Rank-1 feature removal for out-of-distribution detection.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Rankfeat: Rank-1 feature removal for out-of-distribution detection

Reference 30

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Observation 920d69a6-324f-4ba4-971f-e1a6e790794c · outbound

This paper cites Spectral graph the- ory and its applications.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Spectral graph the- ory and its applications

Reference 31

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This paper cites Large margin deep networks for out- of-distribution detection.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Large margin deep networks for out- of-distribution detection

Reference 33

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This paper cites Goodat: Towards test-time graph out-of-distribution detection.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Goodat: Towards test-time graph out-of-distribution detection

Reference 34

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This paper cites A com- prehensive survey on graph neural networks.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps A com- prehensive survey on graph neural networks

Reference 35

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This paper cites Energy-based Out-of-Distribution Detection for Graph Neural Networks.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Energy-based Out-of-Distribution Detection for Graph Neural Networks

Reference 36

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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Graph learning: A survey

Reference 37

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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps How Powerful are Graph Neural Networks?

Reference 38

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This paper cites Openood: Benchmarking generalized out-of-distribution detection.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Openood: Benchmarking generalized out-of-distribution detection

Reference 40

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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Graph con- trastive learning with augmentations

Reference 41

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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Graph contrastive learning auto- mated

Reference 42

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This paper cites Dual-discriminative graph neural network for imbalanced graph-level anomaly detection.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Dual-discriminative graph neural network for imbalanced graph-level anomaly detection

Reference 43

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This paper cites On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights

Reference 44

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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps gap-only

Reference 45

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Observation a9ae3f9e-7ac5-4e46-b82b-ec640b89cdf0 · outbound

This paper cites fixed” data-level Laplacian and “learned.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps fixed” data-level Laplacian and “learned

Reference 47

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This paper cites Convolutional neural net- works on graphs with fast localized spectral filtering.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Convolutional neural net- works on graphs with fast localized spectral filtering

Reference 1997

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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Open graph benchmark: Datasets for machine learning on graphs

Reference 2006

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Observation 8011f2bb-a229-4a00-af37-9cac84a9a48f · outbound

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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Graph Attention Networks

Reference 2007

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This paper cites A data-centric framework to endow graph neural networks with out-of- distribution detection ability.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps A data-centric framework to endow graph neural networks with out-of- distribution detection ability

Reference 2010

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Observation 16c53a27-011c-481f-88b6-0a3dffedc802 · outbound

This paper cites Lee, and Yuval Peres.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Lee, and Yuval Peres

Reference 2016

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Observation b1289217-a360-4d52-8842-f1c2afd5499c · outbound

This paper cites A baseline for detecting misclassified and out-of- distribution examples in neural networks.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps A baseline for detecting misclassified and out-of- distribution examples in neural networks

Reference 2017

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Observation 33d4f645-1801-4cef-9b43-b6a1d5831eef · outbound

This paper cites Generalized Out-of-Distribution Detection: A Survey.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Generalized Out-of-Distribution Detection: A Survey

Reference 2018

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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Graphde: A generative framework for debiased learn- ing and out-of-distribution detection on graphs

Reference 2019

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Observation 265a9e8e-4e8f-4915-b3d7-adfb155ec505 · outbound

This paper cites Praga: Prototype-aware graph adaptive aggregation for spatial multi-modal omics analysis.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Praga: Prototype-aware graph adaptive aggregation for spatial multi-modal omics analysis

Reference 2020

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Observation 0b08f09a-2c62-4658-968e-1083bbc53697 · outbound

This paper cites Label efficient semi- supervised learning via graph filtering.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Label efficient semi- supervised learning via graph filtering

Reference 2021

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Observation 2d568418-f087-476b-b553-30978db648a7 · outbound

This paper cites Enhancing the reliability of out-of-distribution image detection in neural networks.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Enhancing the reliability of out-of-distribution image detection in neural networks

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:29:03.499341Z

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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Inductive representation learning on large graphs

Reference 2023

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Observation 4dde5fc6-a735-4f8e-873b-3090b4fcee86 · outbound

This paper cites Drugood: Out-of- distribution dataset curator and benchmark for ai-aided drug discovery–a focus on affinity prediction problems with noise annotations.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Drugood: Out-of- distribution dataset curator and benchmark for ai-aided drug discovery–a focus on affinity prediction problems with noise annotations

Reference 2024

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Observation 2ffce582-33b4-4a4b-aec6-0a695a8c6c21 · outbound

This paper cites Raising the Bar in Graph-level Anomaly Detection.

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps Raising the Bar in Graph-level Anomaly Detection

Reference 2025

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Pith citing papers

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