Paper Citation Record · LEDGER

Scalable Explanation of Inferences on Large Graphs

As of 21 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:1908.06482.

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

pith.paper-citation-record.v1
1908.06482 v2

Coverage vector

measured 51 of 51 reference resolution

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measured 51 of 51 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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Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 284f70c0-f6bb-4619-a9da-ae6e67b9bb79 · outbound

This paper cites On the approximability of minimizing nonzero variables or unsatisﬁed relations in linear systems.

Scalable Explanation of Inferences on Large Graphs On the approximability of minimizing nonzero variables or unsatisﬁed relations in linear systems

Reference 1

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Observation 0fe74f50-87e9-45fc-bd5d-8ca127f8f374 · outbound

This paper cites How to explain individual classiﬁcation decisions.

Scalable Explanation of Inferences on Large Graphs How to explain individual classiﬁcation decisions

Reference 2

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Observation 8a7571db-a51c-4343-971a-e2fb12f0b90b · outbound

This paper cites Diverse m-best solutions in markov random ﬁelds.

Scalable Explanation of Inferences on Large Graphs Diverse m-best solutions in markov random ﬁelds

Reference 3

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Observation 94e61bd2-c5ec-4ad6-9b14-da38b912d6f8 · outbound

This paper cites Case-based explanation of non-case-based learning methods.

Scalable Explanation of Inferences on Large Graphs Case-based explanation of non-case-based learning methods

Reference 4

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Observation 366e5ae2-ced7-4fef-a031-327b3cf728a7 · outbound

This paper cites Sensitivity analysis in markov networks.

Scalable Explanation of Inferences on Large Graphs Sensitivity analysis in markov networks

Reference 5

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Observation ef11865d-8929-4fd3-a4c6-b5f4c2aeda25 · outbound

This paper cites Reading tea leaves: How humans interpret topic models.

Scalable Explanation of Inferences on Large Graphs Reading tea leaves: How humans interpret topic models

Reference 6

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Observation 73cf1b56-bbbc-4ed9-93b1-2a9e3f82c177 · outbound

This paper cites A differential approach to inference in bayesian networks.

Scalable Explanation of Inferences on Large Graphs A differential approach to inference in bayesian networks

Reference 7

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Observation edfe845b-8434-43f5-8757-90f061ca2031 · outbound

This paper cites Sparse inverse covariance estimation with the graphical lasso.

Scalable Explanation of Inferences on Large Graphs Sparse inverse covariance estimation with the graphical lasso

Reference 8

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Observation 17e7c3d6-edd2-472b-aecf-5e4536458e12 · outbound

This paper cites Residual splash for optimally parallelizing belief propagation.

Scalable Explanation of Inferences on Large Graphs Residual splash for optimally parallelizing belief propagation

Reference 9

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Observation 77fa244e-e963-40c7-af24-6511f44cf92b · outbound

This paper cites European union regulations on algorithmic decision-making and a right to explanation.

Scalable Explanation of Inferences on Large Graphs European union regulations on algorithmic decision-making and a right to explanation

Reference 10

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Observation 7e54b94e-057f-4ebf-acf8-9493f54db9f8 · outbound

This paper cites An introduction to variable and feature selection.

Scalable Explanation of Inferences on Large Graphs An introduction to variable and feature selection

Reference 11

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Source-reported events for the cited work

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Observation cb3ee1b9-2033-4db5-820e-c75b30c8b9bd · outbound

This paper cites Norm-product belief propa- gation: Primal-dual message-passing for approximate inference.

Scalable Explanation of Inferences on Large Graphs Norm-product belief propa- gation: Primal-dual message-passing for approximate inference

Reference 12

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Observation 44eb9c76-2892-4a0f-a59f-ce57279e3604 · outbound

This paper cites Variable selection for gaussian graphical models.

Scalable Explanation of Inferences on Large Graphs Variable selection for gaussian graphical models

Reference 13

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Observation ac3156a7-63b7-427f-b4e9-e19e70a3df18 · outbound

This paper cites An efﬁcient network querying method based on conditional random ﬁelds.

Scalable Explanation of Inferences on Large Graphs An efﬁcient network querying method based on conditional random ﬁelds

Reference 14

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Observation 6d14f9d7-323c-4380-b2f5-8147f78287f7 · outbound

This paper cites Fast and scalable distributed loopy belief propagation on real-world graphs.

Scalable Explanation of Inferences on Large Graphs Fast and scalable distributed loopy belief propagation on real-world graphs

Reference 15

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Observation 571c6362-fbb8-4496-a63f-01c254f83f09 · outbound

This paper cites Composing graphical models with neural networks for structured representations and fast in- ference.

Scalable Explanation of Inferences on Large Graphs Composing graphical models with neural networks for structured representations and fast in- ference

Reference 16

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Observation d383e8f8-f742-4ab3-ad56-0d204e568e89 · outbound

This paper cites Inference of beliefs on billion-scale graphs.

Scalable Explanation of Inferences on Large Graphs Inference of beliefs on billion-scale graphs

Reference 17

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Observation 818b2cbb-7503-4da1-8059-7e6d6d3bb537 · outbound

This paper cites Markov random ﬁelds and their applications.

Scalable Explanation of Inferences on Large Graphs Markov random ﬁelds and their applications

Reference 18

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Observation 430a3e46-d8f7-4339-8869-4ce36c8e1c2f · outbound

This paper cites Understanding black-box predictions via inﬂuence functions.

Scalable Explanation of Inferences on Large Graphs Understanding black-box predictions via inﬂuence functions

Reference 19

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This paper cites Probabilistic graphical models: principles and techniques.

Scalable Explanation of Inferences on Large Graphs Probabilistic graphical models: principles and techniques

Reference 20

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This paper cites Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models.

Scalable Explanation of Inferences on Large Graphs Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models

Reference 21

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This paper cites An Evaluation of the Human-Interpretability of Explanation.

Scalable Explanation of Inferences on Large Graphs An Evaluation of the Human-Interpretability of Explanation

Reference 22

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Observation 5d3f8603-7b05-44fd-903f-60efded385c5 · outbound

This paper cites Rationalizing Neural Predictions.

Scalable Explanation of Inferences on Large Graphs Rationalizing Neural Predictions

Reference 23

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Observation b1322f1e-d2a2-41eb-ae45-3a74e40a43e1 · outbound

This paper cites Protein– protein interaction site prediction based on conditional random ﬁelds.

Scalable Explanation of Inferences on Large Graphs Protein– protein interaction site prediction based on conditional random ﬁelds

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:41:57.758905Z

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Observation 5e2ec52e-7598-49c5-a4a1-e4d80ac6f16d · outbound

This paper cites The structure and function of explanations.

Scalable Explanation of Inferences on Large Graphs The structure and function of explanations

Reference 25

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Observation c94c6309-5233-447b-bd61-b504c0cb562b · outbound

This paper cites Intelligible models for classiﬁcation and regression.

Scalable Explanation of Inferences on Large Graphs Intelligible models for classiﬁcation and regression

Reference 26

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Observation a87d17b7-87e2-419d-9826-9f9ff935e6a4 · outbound

This paper cites Reviews, reputation, and revenue: The case of yelp.

Scalable Explanation of Inferences on Large Graphs Reviews, reputation, and revenue: The case of yelp

Reference 27

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Observation 2fd821ab-b3b4-408f-ba65-6424b3e0a08f · outbound

This paper cites The magical number seven, plus or minus two: Some limits on our capacity for processing information.

Scalable Explanation of Inferences on Large Graphs The magical number seven, plus or minus two: Some limits on our capacity for processing information

Reference 28

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Observation 960ea799-b933-4bb4-ba2c-97c1216333a4 · outbound

This paper cites Collective graph identiﬁcation.

Scalable Explanation of Inferences on Large Graphs Collective graph identiﬁcation

Reference 29

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Observation 5a574e78-7420-4f91-a264-b3313014ec6e · outbound

This paper cites An analysis of approximations for maximizing submod- ular set functions.

Scalable Explanation of Inferences on Large Graphs An analysis of approximations for maximizing submod- ular set functions

Reference 30

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Observation e74d008a-c341-4389-af2b-d276558bee79 · outbound

This paper cites An explanation mechanism for bayesian inferencing systems.

Scalable Explanation of Inferences on Large Graphs An explanation mechanism for bayesian inferencing systems

Reference 31

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This paper cites Probabilistic reasoning in intelligent systems: networks of plausible inference.

Scalable Explanation of Inferences on Large Graphs Probabilistic reasoning in intelligent systems: networks of plausible inference

Reference 32

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Scalable Explanation of Inferences on Large Graphs Deepwalk: Online learning of social representations

Reference 33

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Observation 87882de0-dc03-42bb-885a-d8b30aaac3d2 · outbound

This paper cites Collective opinion spam detection: Bridging review networks and metadata.

Scalable Explanation of Inferences on Large Graphs Collective opinion spam detection: Bridging review networks and metadata

Reference 34

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Observation 5bdded5b-5999-45e9-a9ea-88d856038e0b · outbound

This paper cites Why should i trust you?: Explaining the predictions of any classiﬁer.

Scalable Explanation of Inferences on Large Graphs Why should i trust you?: Explaining the predictions of any classiﬁer

Reference 35

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Observation ee21ac19-aa0d-4784-9b37-71ea4895fd73 · outbound

This paper cites Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations.

Scalable Explanation of Inferences on Large Graphs Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Reference 36

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Scalable Explanation of Inferences on Large Graphs Efficient Search for Diverse Coherent Explanations

Reference 37

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Scalable Explanation of Inferences on Large Graphs Constructing the call graph of a pro- gram

Reference 38

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Observation a2a65e5e-e0e2-451a-9013-cf3abdd9d320 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Scalable Explanation of Inferences on Large Graphs Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 39

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Scalable Explanation of Inferences on Large Graphs Integrating rich user feedback into intelligent user interfaces

Reference 40

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Scalable Explanation of Inferences on Large Graphs Explanation in bayesian belief networks

Reference 41

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This paper cites An introduction to conditional random ﬁelds.

Scalable Explanation of Inferences on Large Graphs An introduction to conditional random ﬁelds

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:41:57.548528Z

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Scalable Explanation of Inferences on Large Graphs Relational learning via latent social dimensions

Reference 43

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Scalable Explanation of Inferences on Large Graphs Learning to infer social ties in large networks

Reference 44

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Observation 2dcace2f-17b6-4c00-b3d1-9e06be1cb9fe · outbound

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Scalable Explanation of Inferences on Large Graphs An analysis of physi- cian attitudes regarding computer-based clinical consultation systems

Reference 45

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Observation d8ab04e5-50e9-4401-b7c7-a0a6a93da7cb · outbound

This paper cites Counter- factual explanations without opening the black box: Automated decisions and the gpdr.

Scalable Explanation of Inferences on Large Graphs Counter- factual explanations without opening the black box: Automated decisions and the gpdr

Reference 46

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This paper cites inﬂuence sketching: Finding inﬂuential samples in large-scale regressions.

Scalable Explanation of Inferences on Large Graphs inﬂuence sketching: Finding inﬂuential samples in large-scale regressions

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:41:57.470217Z

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Observation b79d283a-2fd2-47b2-a270-bb128a1507d0 · outbound

This paper cites Linear program- ming relaxations and belief propagation–an empirical study.

Scalable Explanation of Inferences on Large Graphs Linear program- ming relaxations and belief propagation–an empirical study

Reference 48

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Observation a1cf0203-bdcc-40c8-a597-11b2ddd6b38e · outbound

This paper cites Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks.

Scalable Explanation of Inferences on Large Graphs Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks

Reference 49

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Scalable Explanation of Inferences on Large Graphs Predicting multicellular function through multi-layer tissue networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:41:57.440448Z

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Scalable Explanation of Inferences on Large Graphs Unresolved cited work

Reference 212

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Pith citing papers

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