Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework | Paper Citation Record

Observation 64a15e6f-9e1f-42c9-87f4-2174782dd902 · outbound

This paper cites Detecting Fake News Using Machine Learning : A Systematic Literature Review.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Detecting Fake News Using Machine Learning : A Systematic Literature Review

Reference 1

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This paper cites Downstream trade-offs of a family of text watermarks.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Downstream trade-offs of a family of text watermarks

Reference 2

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Unresolved cited work

Reference 3

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This paper cites Explainability Techniques for Graph Convolutional Networks.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Explainability Techniques for Graph Convolutional Networks

Reference 4

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This paper cites Glimpse: Enabling white-box methods to use proprietary models for zero-shot llm-generated text detection, 2025.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Glimpse: Enabling white-box methods to use proprietary models for zero-shot llm-generated text detection, 2025

Reference 5

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This paper cites Fast-detectgpt: Efficient zero-shot detection of machine-generated text via conditional probability curvature.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Fast-detectgpt: Efficient zero-shot detection of machine-generated text via conditional probability curvature

Reference 6

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This paper cites A neural probabilistic language model.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework A neural probabilistic language model

Reference 7

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This paper cites Springer, 2006.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Springer, 2006

Reference 8

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This paper cites PostMark: A Robust Blackbox Watermark for Large Language Models.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework PostMark: A Robust Blackbox Watermark for Large Language Models

Reference 9

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This paper cites Combating misinformation in the age of llms: Opportunities and challenges.AI Magazine, 45(3):354–368, 2024.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Combating misinformation in the age of llms: Opportunities and challenges.AI Magazine, 45(3):354–368, 2024

Reference 10

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Rev: Information-theoretic evaluation of free-text rationales

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This paper cites Generating in-distribution proxy graphs for explaining graph neural networks.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Generating in-distribution proxy graphs for explaining graph neural networks

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

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 13

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework OpenPrompt: An Open-source Framework for Prompt-learning

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This paper cites RAID: A shared benchmark for robust evaluation of machine-generated text detectors.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework RAID: A shared benchmark for robust evaluation of machine-generated text detectors

Reference 15

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Graphsvx: Shapley value explanations for graph neural networks

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This paper cites Sequential Integrated Gradients: a simple but effective method for explaining language models.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Sequential Integrated Gradients: a simple but effective method for explaining language models

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Coopera- tive explanations of graph neural networks

Reference 18

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This paper cites GLTR: Statistical Detection and Visualization of Generated Text.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework GLTR: Statistical Detection and Visualization of Generated Text

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This paper cites How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection

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This paper cites Biscope: Ai-generated text detection by checking memorization of preceding tokens.Advances in Neural Information Processing Systems, 37:104065–104090, 2024.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Biscope: Ai-generated text detection by checking memorization of preceding tokens.Advances in Neural Information Processing Systems, 37:104065–104090, 2024

Reference 21

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This paper cites DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework A benchmark for inter- pretability methods in deep neural networks

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This paper cites Radar: Robust ai-text detection via adversarial learning.Advances in neural information processing systems, 36:15077–15095, 2023.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Radar: Robust ai-text detection via adversarial learning.Advances in neural information processing systems, 36:15077–15095, 2023

Reference 24

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Automatic Detection of Generated Text is Easiest when Humans are Fooled

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Attention is not explanation

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This paper cites PubMedQA: A Dataset for Biomedical Research Question Answering.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework PubMedQA: A Dataset for Biomedical Research Question Answering

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This paper cites Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Adam: A Method for Stochastic Optimization

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Outfox: Llm-generated essay detection through in-context learning with adversarially generated examples

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework The MIT Press, 2009

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This paper cites Do language models plagiarize? In Proceedings of the ACM Web Conference 2023, pages 3637–3647, 2023.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Do language models plagiarize? In Proceedings of the ACM Web Conference 2023, pages 3637–3647, 2023

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Understanding Neural Networks through Representation Erasure

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework MAGE: Machine-generated Text Detection in the Wild

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Generative causal explanations for graph neural networks

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework RoBERTa: A Robustly Optimized BERT Pretraining Approach

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This paper cites Parameterized explainer for graph neural network.Advances in neural information processing systems, 33:19620–19631, 2020.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Parameterized explainer for graph neural network.Advances in neural information processing systems, 33:19620–19631, 2020

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This paper cites Zero-shot detection of llm-generated text using token cohesiveness.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Zero-shot detection of llm-generated text using token cohesiveness

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Raidar: geneRative AI Detection viA Rewriting

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This paper cites Detectgpt: Zero-shot machine-generated text detection using probability curvature.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Detectgpt: Zero-shot machine-generated text detection using probability curvature

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework WaterJudge: Quality-Detection Trade-off when Watermarking Large Language Models

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This paper cites Simllm: Detecting sentences generated by large language models using similarity between the generation and its re-generation.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Simllm: Detecting sentences generated by large language models using similarity between the generation and its re-generation

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Unresolved cited work

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This paper cites Explainability methods for graph convolutional neural networks.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Explainability methods for graph convolutional neural networks

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This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework A consistent and efficient evaluation strategy for attribution methods

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This paper cites Reinforcement learning enhanced explainer for graph neural networks.Advances in Neural Information Processing Systems, 34:22523–22533, 2021.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Reinforcement learning enhanced explainer for graph neural networks.Advances in Neural Information Processing Systems, 34:22523–22533, 2021

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This paper cites Explaining black box text modules in natural language with language models.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Explaining black box text modules in natural language with language models

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Release Strategies and the Social Impacts of Language Models

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This paper cites Deep kernel relative test for machine-generated text detection.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Deep kernel relative test for machine-generated text detection

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This paper cites Few-shot detection of machine-generated text using style representations.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Few-shot detection of machine-generated text using style representations

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This paper cites DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text

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Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Axiomatic attribution for deep networks

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Observation a55cbbd1-e7d5-4ef0-9264-4889fbb68f44 · outbound

This paper cites Detecting machine-generated long-form content with latent-space variables.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Detecting machine-generated long-form content with latent-space variables

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:56.224684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 06d4ea8d-0bfe-4f3e-8a14-495bcf53987f · outbound

This paper cites GPTZero.https://gptzero.me, 2023.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework GPTZero.https://gptzero.me, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:56.187219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f227d8da-6776-4f92-96de-a7dff96fe6bf · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework LLaMA: Open and Efficient Foundation Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:54.281104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 468dec32-85d7-4c62-adc7-d9a0be4e8e53 · outbound

This paper cites Ghostbuster: Detecting Text Ghostwritten by Large Language Models.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Ghostbuster: Detecting Text Ghostwritten by Large Language Models

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Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:54.284356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e2c6a54-70a5-4fbe-9855-bfb3bf8bed11 · outbound

This paper cites Pgm-explainer: Probabilistic graphical model explanations for graph neural networks.Advances in neural information processing systems, 33:12225–12235, 2020.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Pgm-explainer: Probabilistic graphical model explanations for graph neural networks.Advances in neural information processing systems, 33:12225–12235, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:56.176544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5a4ce117-2d45-4c7c-bd81-81666ecc7900 · outbound

This paper cites Towards multi- grained explainability for graph neural networks.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Towards multi- grained explainability for graph neural networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:56.166206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 693bd465-08f4-4cb8-b4df-9eeba44665c3 · outbound

This paper cites Towards multi-grained explainability for graph neural networks.Advances in Neural Information Processing Systems, 34:18446–18458, 2021.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Towards multi-grained explainability for graph neural networks.Advances in Neural Information Processing Systems, 34:18446–18458, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:56.124804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 845ef8a5-2783-4889-80b9-ec3c4746e248 · outbound

This paper cites Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:56.008458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0f74bc2c-e63e-4e22-8997-c3cc10810358 · outbound

This paper cites M4: Multi-generator, multi- domain, and multi-lingual black-box machine-generated text detection.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework M4: Multi-generator, multi- domain, and multi-lingual black-box machine-generated text detection

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.996809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 643932e7-4ce9-4ed9-b75b-9a90d2c437bf · outbound

This paper cites Attention is not not explanation.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Attention is not not explanation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.986600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0948c951-a753-4084-bb5d-b06534eac62e · outbound

This paper cites A sur- vey on llm-generated text detection: Necessity, methods, and future directions.Computational Linguistics, pages 1–66, 2025.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework A sur- vey on llm-generated text detection: Necessity, methods, and future directions.Computational Linguistics, pages 1–66, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.975881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8785fdf0-152e-4613-a7bb-2284781f8fbc · outbound

This paper cites A resilient and accessible distribution-preserving watermark for large language models.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework A resilient and accessible distribution-preserving watermark for large language models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.964507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb8a21e3-30a1-4b8c-9057-efe25bff54d5 · outbound

This paper cites Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:54.314757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50d32b66-2e08-4f81-99cb-ff7440c4ea99 · outbound

This paper cites Task-agnostic graph explanations.Advances in Neural Information Processing Systems, 35:12027–12039, 2022.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Task-agnostic graph explanations.Advances in Neural Information Processing Systems, 35:12027–12039, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.953630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f4dd717a-dcdd-429c-b1b1-e390ebf53515 · outbound

This paper cites Watermarking Text Generated by Black-Box Language Models.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Watermarking Text Generated by Black-Box Language Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:54.453648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a17bf6d4-ca7a-47e0-bdea-33368bcebcc4 · outbound

This paper cites Dna-gpt: Divergent n-gram analysis for training-free detection of gpt-generated text.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Dna-gpt: Divergent n-gram analysis for training-free detection of gpt-generated text

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.876170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6aae0946-3f4b-493b-a713-bc83e25c6f15 · outbound

This paper cites Graph convolutional networks for text classi- fication.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Graph convolutional networks for text classi- fication

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.834400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 187e8fa2-03c4-4ba8-8761-cf127db67e7b · outbound

This paper cites Gnnexplainer: Generating explanations for graph neural networks.Advances in neural information processing systems, 32, 2019.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Gnnexplainer: Generating explanations for graph neural networks.Advances in neural information processing systems, 32, 2019

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Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:54.576325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 651b709d-a688-4021-9dbd-d642e179521b · outbound

This paper cites Text fluoroscopy: Detecting llm-generated text through intrinsic features.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Text fluoroscopy: Detecting llm-generated text through intrinsic features

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.815423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aaac6f29-b348-4d9c-a071-e0d3c7a5dbee · outbound

This paper cites Wordcraft: story writing with large language models.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Wordcraft: story writing with large language models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.801912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4610b326-2057-425a-b4ce-8fbc1bc58f11 · outbound

This paper cites Xgnn: Towards model-level explanations of graph neural networks.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Xgnn: Towards model-level explanations of graph neural networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.756494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5dfa51c9-68b1-4fca-9a5a-91f47399b629 · outbound

This paper cites Explainability in graph neural networks: A taxonomic survey.IEEE transactions on pattern analysis and machine intelligence, 45(5):5782– 5799, 2022.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Explainability in graph neural networks: A taxonomic survey.IEEE transactions on pattern analysis and machine intelligence, 45(5):5782– 5799, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.624279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 04fa3723-6363-414a-a460-e5d5e2f6fbf1 · outbound

This paper cites Dald: Improving logits-based detector without logits from black-box llms.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Dald: Improving logits-based detector without logits from black-box llms

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3c6b950b-7601-4acb-99fc-61ec959a85ba · outbound

This paper cites Detecting machine-generated texts by multi-population aware optimization for maximum mean discrep- ancy.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Detecting machine-generated texts by multi-population aware optimization for maximum mean discrep- ancy

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8cc4455b-0d13-4637-81e1-e5899e0d77f9 · outbound

This paper cites Detecting machine-generated texts by multi-population aware optimization for maximum mean discrep- ancy.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Detecting machine-generated texts by multi-population aware optimization for maximum mean discrep- ancy

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:38:55.583882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation db46ff73-59f5-41fe-ba78-a0b310f938d8 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework OPT: Open Pre-trained Transformer Language Models

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Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:54.607565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65eaba4e-8b1d-4aa8-abf8-96323c73baa3 · outbound

This paper cites PyBench: Evaluating LLM Agent on various real-world coding tasks.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework PyBench: Evaluating LLM Agent on various real-world coding tasks

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:54.611335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ee2361f-ac52-4ca7-8e9e-f9dd60dbb01b · outbound

This paper cites Explainability for large language models: A survey.ACM Transactions on Intelligent Systems and Technology, 15(2):1–38, 2024.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Explainability for large language models: A survey.ACM Transactions on Intelligent Systems and Technology, 15(2):1–38, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:54.622429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2320194d-6aec-4dfb-a2c7-e2e4426be922 · outbound

This paper cites Toolqa: A dataset for llm question answering with external tools.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework Toolqa: A dataset for llm question answering with external tools

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Resolution: verified exact; raw_fallback, observed 2026-08-15T20:38:55.017014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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