Paper Citation Record · LEDGER

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning

As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2604.14746.

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

pith.paper-citation-record.v1
2604.14746 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T11:03:28.089449Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

30 of 30 outbound references displayed

verified exact6
verified fuzzy23
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9ce955a-5e99-470b-b2b3-3113889839c0 · outbound

This paper cites Deep representation learning for social network analysis.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Deep representation learning for social network analysis

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.454645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c3321b9-0706-4885-be3d-1f76c0d8e310 · outbound

This paper cites Gaugllm: Improving graph contrastive learning for text-attributed graphs with large language models.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Gaugllm: Improving graph contrastive learning for text-attributed graphs with large language models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.458654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation df9c5063-bde5-4a37-9508-00f476fd272a · outbound

This paper cites A comprehensive survey on graph neural networks.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning A comprehensive survey on graph neural networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.464374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 18c21661-319e-4c05-a3be-4c9322e27b53 · outbound

This paper cites A survey of synthetic data augmentation methods in computer vision.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning A survey of synthetic data augmentation methods in computer vision

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:05:08.482583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 21926265-a0ba-48a9-a4f3-f6acc8479d30 · outbound

This paper cites Graph self-supervised learning: A survey.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Graph self-supervised learning: A survey

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.470101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a7e7838-8eda-4701-a5f4-a9945557c93a · outbound

This paper cites Homogcl: Rethinking homophily in graph contrastive learning.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Homogcl: Rethinking homophily in graph contrastive learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.442526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d960211-0d47-490b-86ec-692bb4bc803c · outbound

This paper cites Sim- grace: A simple framework for graph contrastive learning without data augmentation.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Sim- grace: A simple framework for graph contrastive learning without data augmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.429925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 11376688-b2f4-4e9d-b44f-bc597853bf86 · outbound

This paper cites Ergnn: Spectral graph neural network with explicitly-optimized rational graph filters.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Ergnn: Spectral graph neural network with explicitly-optimized rational graph filters

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.438402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 15ea55c1-3f1e-4901-b2e3-95dfa1d17c5c · outbound

This paper cites Deep Graph Contrastive Representation Learning.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Deep Graph Contrastive Representation Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:05:08.474715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 19c95a64-e23b-456a-b388-2cab9b2024d3 · outbound

This paper cites Contrastive multi- view representation learning on graphs.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Contrastive multi- view representation learning on graphs

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.452798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 759357b6-8f18-4131-8c63-28a34fb89824 · outbound

This paper cites The pagerank citation ranking: Bringing order to the web.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning The pagerank citation ranking: Bringing order to the web

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.440550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ca68952-6e37-474c-92b6-34071ca62ada · outbound

This paper cites Towards Graph Contrastive Learning: A Survey and Beyond.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Towards Graph Contrastive Learning: A Survey and Beyond

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:05:08.472030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 27659dc9-22ee-4327-ad26-44912e83a450 · outbound

This paper cites Node feature extraction by self-supervised multi-scale neighborhood prediction.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Node feature extraction by self-supervised multi-scale neighborhood prediction

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.444719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2846e20c-eede-4ade-816b-cdede8055e3a · outbound

This paper cites LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:05:08.485157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a603efb0-3c7e-472b-989b-bd95739f3b0e · outbound

This paper cites Graph-based unsupervised disentangled representation learning via multimodal large language models.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Graph-based unsupervised disentangled representation learning via multimodal large language models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.444543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 593343ed-7550-4c4b-8cfc-a337f5227ad0 · outbound

This paper cites Benchmarking fairness-aware graph neural networks in knowledge graphs.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Benchmarking fairness-aware graph neural networks in knowledge graphs

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:05:08.477386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 61675024-2cac-404d-8eda-7abc9c50942c · outbound

This paper cites Infonce is a free lunch for semantically guided graph contrastive learn- ing.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Infonce is a free lunch for semantically guided graph contrastive learn- ing

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.450776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e5e8821b-efb8-4853-b3ad-8933109dd681 · outbound

This paper cites Large-scale representation learning on graphs via bootstrap- ping.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Large-scale representation learning on graphs via bootstrap- ping

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.456656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 347df8d6-ce4c-4697-8dad-d4644a1e9721 · outbound

This paper cites Refining interactions: Enhancing anisotropy in graph neural networks with language semantics.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Refining interactions: Enhancing anisotropy in graph neural networks with language semantics

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.460566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 394e6c82-bf70-4913-97ee-845604cf323e · outbound

This paper cites Exploring the over-smoothing problem of graph neural networks for graph classification: an entropy-based viewpoint.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Exploring the over-smoothing problem of graph neural networks for graph classification: an entropy-based viewpoint

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.433841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 17648109-f307-43d0-af47-ad90006a7d42 · outbound

This paper cites Polygcl: Graph contrastive learning via learnable spectral polynomial filters.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Polygcl: Graph contrastive learning via learnable spectral polynomial filters

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.425682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad27ac90-beb0-43cc-847f-75b07236383a · outbound

This paper cites Fine-grained semantics enhanced contrastive learning for graphs.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Fine-grained semantics enhanced contrastive learning for graphs

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.467982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 15720105-d0ad-4ff5-bdb7-72ac65d08001 · outbound

This paper cites Leveraging graph structures to detect hallucinations in large language models.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Leveraging graph structures to detect hallucinations in large language models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.436100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a016bef6-a356-45d1-a1e0-789448ca7aa0 · outbound

This paper cites Minimal variance sampling with provable guarantees for fast training of graph neural networks.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Minimal variance sampling with provable guarantees for fast training of graph neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.432106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 16291c16-f88e-4446-a3d3-90f349a5c1e8 · outbound

This paper cites Size does (not) matter? sparsification and graph neural network sampling for large-scale graphs.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Size does (not) matter? sparsification and graph neural network sampling for large-scale graphs

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.446822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8388ae5f-522f-4898-be44-c04b8c543223 · outbound

This paper cites Collective classification in network data.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Collective classification in network data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.466169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94592973-37b5-4daa-a5f0-56689f84a5bc · outbound

This paper cites Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:05:08.487784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 681cabe9-93b5-45ca-a1fa-275be19659f0 · outbound

This paper cites Collective classification in network data.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Collective classification in network data

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.448710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0316a6d8-f83e-46a6-83ab-8628942e0dc7 · outbound

This paper cites Justifying recommen- dations using distantly-labeled reviews and fine-grained aspects.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning Justifying recommen- dations using distantly-labeled reviews and fine-grained aspects

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T15:53:08.462633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 085b3c58-622f-406e-a129-ab8389b6bb26 · outbound

This paper cites The Llama 3 Herd of Models.

Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning The Llama 3 Herd of Models

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-10T11:05:08.480038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.