Paper Citation Record · LEDGER

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions

As of 24 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.07611.

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

pith.paper-citation-record.v1
2607.07611 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-09T05:13:39.338987Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

32 of 32 outbound references displayed

verified exact2
verified fuzzy29
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25d74ad0-662c-48fb-9ae0-eeda8feca650 · outbound

This paper cites The Rising Tide of Polypharmacy and Drug-Drug Interactions: Population Database Analysis 1995–2010.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions The Rising Tide of Polypharmacy and Drug-Drug Interactions: Population Database Analysis 1995–2010

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.345193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f8055b1d-4ffd-4cb0-8275-1efb2f49c7c7 · outbound

This paper cites Clinical Consequences of Polypharmacy in Elderly.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Clinical Consequences of Polypharmacy in Elderly

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.324541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7b54ef49-c631-4e82-9735-cb8c267d532c · outbound

This paper cites Population-Scale Identification of Differential Adverse Events before and During a Pandemic.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Population-Scale Identification of Differential Adverse Events before and During a Pandemic

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.331126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c2659499-4dac-4fa5-b95c-30991497d331 · outbound

This paper cites Modeling Polypharmacy Side Effects with Graph Convolutional Networks.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Modeling Polypharmacy Side Effects with Graph Convolutional Networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.306480Z

Source-reported events for the cited work

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Observation 7ad83b5d-bf3b-41ec-8309-c2d7a61e9896 · outbound

This paper cites Emergency Hospitalizations for Adverse Drug Events in Older Americans.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Emergency Hospitalizations for Adverse Drug Events in Older Americans

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.308424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8a42f517-7df5-4809-a726-0eb15f084e5c · outbound

This paper cites Data -Driven Prediction of Drug Effects and Interactions.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Data -Driven Prediction of Drug Effects and Interactions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.335388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2d7eb42e-2bcb-44ff-a838-a364fc7c601e · outbound

This paper cites Focal Loss for Dense Object Detection.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Focal Loss for Dense Object Detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.313159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b8f09b3-4074-46fd-a90b-5f66efb20d9f · outbound

This paper cites Extended -Connectivity Fingerprints.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Extended -Connectivity Fingerprints

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.321073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0e0e57a2-6735-484d-8969-bdf8a7088fbf · outbound

This paper cites A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators with Functional Group Information and Hypergraph Structure.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators with Functional Group Information and Hypergraph Structure

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.315363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 020e0802-95d6-4f20-9907-06f79711feed · outbound

This paper cites Sumgnn: Multi -Typed Drug Interaction Prediction Via Efficient Knowledge Graph Summarization.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Sumgnn: Multi -Typed Drug Interaction Prediction Via Efficient Knowledge Graph Summarization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.333406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0bccd7ae-6d5e-4c67-8675-786de311b7e4 · outbound

This paper cites Generating Explainable Hypotheses for Drug Repurposing with Graph Neural Networks.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Generating Explainable Hypotheses for Drug Repurposing with Graph Neural Networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.341361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0184d3c7-21b2-4cbc-add6-283af60821e5 · outbound

This paper cites A Comprehensive Survey on Graph Neural Networks.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions A Comprehensive Survey on Graph Neural Networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.309322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 01755dfe-0a89-4d4f-ad5d-917bd6d41cc2 · outbound

This paper cites Towards a Generalizable and Expressive Graph Neural Network for Graph -Level Tasks with Theoretical Guarantees.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Towards a Generalizable and Expressive Graph Neural Network for Graph -Level Tasks with Theoretical Guarantees

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.299526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f7864c48-980f-4a0e-b564-aa830c770df8 · outbound

This paper cites Stacked Bidirectional and Unidirectional Lstm Recurrent Neural Network for Forecasting Network-Wide Traffic State with Missing Values.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Stacked Bidirectional and Unidirectional Lstm Recurrent Neural Network for Forecasting Network-Wide Traffic State with Missing Values

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.305346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c230aad4-cdf9-49d7-a8ed-b154a11b70f1 · outbound

This paper cites Learning Deep Representations for Graph Clustering.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Learning Deep Representations for Graph Clustering

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.312293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3d048eed-b6e3-4a34-9b50-94c6fb058cb1 · outbound

This paper cites Smote: Synthetic Minority over -Sampling Technique.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Smote: Synthetic Minority over -Sampling Technique

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.330950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d14bb580-2aeb-43ec-8b09-1fc944a71287 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-09T05:15:59.132977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 40d5d613-8867-45e1-9818-adf8dac03043 · outbound

This paper cites Attention Is All You Need.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Attention Is All You Need

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.345512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation be5787f0-815a-4f95-bea7-4d37161a511b · outbound

This paper cites Feasibility of the Soft Attention-Based Models for Automatic Segmentation of Oct Kidney Images.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Feasibility of the Soft Attention-Based Models for Automatic Segmentation of Oct Kidney Images

Reference 19

Resolution: verified exact; doi, observed 2026-07-09T05:15:59.021741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 80d0f45d-6464-4a38-a234-54e0001a2728 · outbound

This paper cites Soft Attention -Based U -Net for Automatic Segmentation of Oct Kidney Images.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Soft Attention -Based U -Net for Automatic Segmentation of Oct Kidney Images

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.307262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5f207b00-ca94-4fc3-b144-8e0d2d7c0ba9 · outbound

This paper cites Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.311176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 35b6b8c8-afc1-48f1-b0fa-3620858939db · outbound

This paper cites Asymmetric Loss for Multi-Label Classification.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Asymmetric Loss for Multi-Label Classification

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.347439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3da898b9-cadc-4d08-acd5-5f0651f65f48 · outbound

This paper cites Training Region -Based Object Detectors with Online Hard Example Mining.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Training Region -Based Object Detectors with Online Hard Example Mining

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.337464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation be6cb619-edce-4060-aaca-8ce1247e4ce8 · outbound

This paper cites Pubchem 2023 Update.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Pubchem 2023 Update

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.343203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aec3b9cc-b83d-45d0-9b2a-c0cce2ec0a40 · outbound

This paper cites Modeling Relational Data with Graph Convolutional Networks.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Modeling Relational Data with Graph Convolutional Networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.338823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e0f5a69b-e44e-4b1d-ac60-16a218d52157 · outbound

This paper cites Learning Phrase Representations Using Rnn Encoder –Decoder for Statistical Machine Translation.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Learning Phrase Representations Using Rnn Encoder –Decoder for Statistical Machine Translation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.339369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3015ed47-45ae-48c1-8b51-4376437372b5 · outbound

This paper cites Optuna: A Next -Generation Hyperparameter Optimization Framework.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Optuna: A Next -Generation Hyperparameter Optimization Framework

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.336400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b3518c33-23fe-4ae3-9e1f-30a2cb1565f5 · outbound

This paper cites A systematic review of quantum machine learning for digital health.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions A systematic review of quantum machine learning for digital health

Reference 28

Resolution: malformed identifier; doi_truncated, observed 2026-07-09T05:15:59.024736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 70d2b690-7a6a-4990-92a2-90e0569e2ec4 · outbound

This paper cites Skipgnn: Predicting Molecular Interactions with Skip- Graph Networks.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Skipgnn: Predicting Molecular Interactions with Skip- Graph Networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.318141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1c4fabee-67fd-4637-a696-86c7a1ccaa96 · outbound

This paper cites Caster: Predicting Drug Interactions with Chemical Substructure Representation.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Caster: Predicting Drug Interactions with Chemical Substructure Representation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.285628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3d1fb8d5-0bf1-4dbb-90bf-c53cd527f5d8 · outbound

This paper cites Biobert: A Pre - Trained Biomedical Language Representation Model for Biomedical Text Mining.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Biobert: A Pre - Trained Biomedical Language Representation Model for Biomedical Text Mining

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.343585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aee80a92-7f6c-4542-9166-341a4483a8d9 · outbound

This paper cites Drug -Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional -Lstm Network.

Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions Drug -Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional -Lstm Network

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T05:15:59.341208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.