Paper Citation Record · LEDGER

A Probabilistic Model for Node Classification in Directed Graphs

As of 20 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2501.01630.

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

pith.paper-citation-record.v1
2501.01630 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:30:02.138289Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

42 of 42 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c2934d52-fe43-4f07-b11c-dd75c3a67428 · outbound

This paper cites Interpretable predictions for crime categories using log loss approach for imbalanced target feature.

A Probabilistic Model for Node Classification in Directed Graphs Interpretable predictions for crime categories using log loss approach for imbalanced target feature

Reference 1

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Observation 38011894-5f14-41ab-9ff2-10cb110593d3 · outbound

This paper cites A survey on bert and its applications.

A Probabilistic Model for Node Classification in Directed Graphs A survey on bert and its applications

Reference 2

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Observation 9682c578-b1bb-4bf7-96c9-e47f8d98059d · outbound

This paper cites A gentle introduction to deep learning for graphs.

A Probabilistic Model for Node Classification in Directed Graphs A gentle introduction to deep learning for graphs

Reference 3

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This paper cites Belle and I.

A Probabilistic Model for Node Classification in Directed Graphs Belle and I

Reference 4

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This paper cites Interpretability in deep learning for finance: a case study for the heston model, 2021.

A Probabilistic Model for Node Classification in Directed Graphs Interpretability in deep learning for finance: a case study for the heston model, 2021

Reference 5

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Observation 5c32f46f-f4e3-4e31-8cec-6dabd19c2895 · outbound

This paper cites Ap- plications of graph convolutional networks in computer vision.Neural Com- puting and Applications , 34(16):13387–13405, 2022.

A Probabilistic Model for Node Classification in Directed Graphs Ap- plications of graph convolutional networks in computer vision.Neural Com- puting and Applications , 34(16):13387–13405, 2022

Reference 6

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Observation 8d6fe5bc-c37e-49e5-b293-0d213f3cebf1 · outbound

This paper cites Carvalho, Eduardo M.

A Probabilistic Model for Node Classification in Directed Graphs Carvalho, Eduardo M

Reference 7

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Observation 2f0d4711-0e95-4388-ac28-dbf12ae90d97 · outbound

This paper cites Feature selection for text classification with na ¨ ıve bayes.Expert Systems with Ap- plications, 36(3, Part 1):5432–5435, 2009.

A Probabilistic Model for Node Classification in Directed Graphs Feature selection for text classification with na ¨ ıve bayes.Expert Systems with Ap- plications, 36(3, Part 1):5432–5435, 2009

Reference 8

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Observation 617cd419-bf95-44ee-910a-f46715b50647 · outbound

This paper cites Jackknifing documents and additive smoothing for naive bayes with scarce data.

A Probabilistic Model for Node Classification in Directed Graphs Jackknifing documents and additive smoothing for naive bayes with scarce data

Reference 9

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Observation f1417267-c973-48ac-bcfb-a7f3c87bc2fb · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

A Probabilistic Model for Node Classification in Directed Graphs Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:30:02.536727Z

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Observation ea3958a1-ffbf-411a-a6c8-ab06e249243d · outbound

This paper cites A convo- lutional encoder model for neural machine translation.

A Probabilistic Model for Node Classification in Directed Graphs A convo- lutional encoder model for neural machine translation

Reference 11

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Observation 087fe371-6307-4351-86e3-d3f035568c74 · outbound

This paper cites node2vec: Scalable feature learning for networks, 2016.

A Probabilistic Model for Node Classification in Directed Graphs node2vec: Scalable feature learning for networks, 2016

Reference 12

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Observation 2cf2a8a3-7bb8-4f81-9d41-9dff9d43192b · outbound

This paper cites Hamilton.

A Probabilistic Model for Node Classification in Directed Graphs Hamilton

Reference 13

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Observation 4271d2e2-61fd-46e3-a910-f9c235dca462 · outbound

This paper cites Hamilton, Rex Ying, and Jure Leskovec.

A Probabilistic Model for Node Classification in Directed Graphs Hamilton, Rex Ying, and Jure Leskovec

Reference 14

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Observation 9c0591cc-5260-4b8a-b8b3-144e9390c0d4 · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs, 2021.

A Probabilistic Model for Node Classification in Directed Graphs Open graph benchmark: Datasets for machine learning on graphs, 2021

Reference 15

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This paper cites Ben- son.

A Probabilistic Model for Node Classification in Directed Graphs Ben- son

Reference 16

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Observation fb8cb583-e472-40ed-948f-95bac15b17d4 · outbound

This paper cites Toward online node classification on streaming networks.

A Probabilistic Model for Node Classification in Directed Graphs Toward online node classification on streaming networks

Reference 17

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Observation 9d2072c7-9c28-4c05-97a9-2408da336c2e · outbound

This paper cites Anomaly detection with graph convolutional networks for insider threat and fraud detection.

A Probabilistic Model for Node Classification in Directed Graphs Anomaly detection with graph convolutional networks for insider threat and fraud detection

Reference 18

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A Probabilistic Model for Node Classification in Directed Graphs Unresolved cited work

Reference 19

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

A Probabilistic Model for Node Classification in Directed Graphs Semi-Supervised Classification with Graph Convolutional Networks

Reference 20

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This paper cites DeepGCNs: Can GCNs Go as Deep as CNNs?.

A Probabilistic Model for Node Classification in Directed Graphs DeepGCNs: Can GCNs Go as Deep as CNNs?

Reference 21

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Observation e3e19fd1-0eba-4ff1-94ae-d8b6c70193ad · outbound

This paper cites Deep Learning on Graphs.

A Probabilistic Model for Node Classification in Directed Graphs Deep Learning on Graphs

Reference 22

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This paper cites Abusive language detection with graph convolutional networks, 2019.

A Probabilistic Model for Node Classification in Directed Graphs Abusive language detection with graph convolutional networks, 2019

Reference 23

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Observation 7e17e0b9-dbc4-4c94-a0cb-9439fcd74636 · outbound

This paper cites Fast and Accurate Sen- timent Classification Using an Enhanced Naive Bayes Model, page 194–201.

A Probabilistic Model for Node Classification in Directed Graphs Fast and Accurate Sen- timent Classification Using an Enhanced Naive Bayes Model, page 194–201

Reference 24

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This paper cites Networks.

A Probabilistic Model for Node Classification in Directed Graphs Networks

Reference 25

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A Probabilistic Model for Node Classification in Directed Graphs Unresolved cited work

Reference 26

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This paper cites Qader, Musa M.

A Probabilistic Model for Node Classification in Directed Graphs Qader, Musa M

Reference 27

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This paper cites Text mining: Use of tf-idf to examine the relevance of words to documents.

A Probabilistic Model for Node Classification in Directed Graphs Text mining: Use of tf-idf to examine the relevance of words to documents

Reference 28

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Observation ec882c59-98f2-47b9-bea6-b8ae3f84449a · outbound

This paper cites Universality of citation distributions: Toward an objective measure of scientific impact.

A Probabilistic Model for Node Classification in Directed Graphs Universality of citation distributions: Toward an objective measure of scientific impact

Reference 29

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This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

A Probabilistic Model for Node Classification in Directed Graphs Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 30

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A Probabilistic Model for Node Classification in Directed Graphs En- sembles of bert for depression classification

Reference 31

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This paper cites Interpretability of machine learning-based prediction models in healthcare.

A Probabilistic Model for Node Classification in Directed Graphs Interpretability of machine learning-based prediction models in healthcare

Reference 32

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This paper cites Node Classification in Signed Social Networks, pages 54–62.

A Probabilistic Model for Node Classification in Directed Graphs Node Classification in Signed Social Networks, pages 54–62

Reference 33

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This paper cites Are the discretised lognormal and hooked power law distri- butions plausible for citation data? Journal of Informetrics, 10(2):454–470, 2016.

A Probabilistic Model for Node Classification in Directed Graphs Are the discretised lognormal and hooked power law distri- butions plausible for citation data? Journal of Informetrics, 10(2):454–470, 2016

Reference 34

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This paper cites The discretised lognormal and hooked power law distribu- tions for complete citation data: Best options for modelling and regression.

A Probabilistic Model for Node Classification in Directed Graphs The discretised lognormal and hooked power law distribu- tions for complete citation data: Best options for modelling and regression

Reference 35

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This paper cites Attention Is All You Need.

A Probabilistic Model for Node Classification in Directed Graphs Attention Is All You Need

Reference 36

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A Probabilistic Model for Node Classification in Directed Graphs Dickerson, and Su-In Lee

Reference 37

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This paper cites Naive bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation.

A Probabilistic Model for Node Classification in Directed Graphs Naive bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation

Reference 38

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This paper cites Research on the application of deep learning-based bert model in sentiment analysis, 2024.

A Probabilistic Model for Node Classification in Directed Graphs Research on the application of deep learning-based bert model in sentiment analysis, 2024

Reference 39

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A Probabilistic Model for Node Classification in Directed Graphs A comprehensive survey on graph neural networks

Reference 40

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This paper cites Bayesian na ¨ ıve bayes classifiers to text classification.Journal of Information Science, 44(1):48–59, 2016.

A Probabilistic Model for Node Classification in Directed Graphs Bayesian na ¨ ıve bayes classifiers to text classification.Journal of Information Science, 44(1):48–59, 2016

Reference 41

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This paper cites Graph con- volutional networks: a comprehensive review.

A Probabilistic Model for Node Classification in Directed Graphs Graph con- volutional networks: a comprehensive review

Reference 42

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Pith citing papers

No inbound Pith citation observations are available.