Paper Citation Record · LEDGER

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning

As of 15 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 2 inbound Pith citation observations for arXiv:2507.09132.

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

pith.paper-citation-record.v1
2507.09132 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:12:27.928253Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T19:16:15.616715Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T14:55:47.461885Z

Reference resolution

58 of 58 outbound references displayed

verified exact2
verified fuzzy49
unresolved7
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 22d175e2-cdbb-4bd2-904e-84a2c6ea92fe · outbound

This paper cites A survey of graph neural network based recommendation in social networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning A survey of graph neural network based recommendation in social networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.982435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 55f5dc42-12b0-4842-a177-0ba6919473bc · outbound

This paper cites Multi-behavior graph neural networks for recommender system,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Multi-behavior graph neural networks for recommender system,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.961771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c20329d9-8f11-401b-9ede-8f5930513aab · outbound

This paper cites Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.938415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 70e04664-0509-437f-9bef-37bfde93833b · outbound

This paper cites Illuminati: Towards explaining graph neural networks for cybersecurity analysis,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Illuminati: Towards explaining graph neural networks for cybersecurity analysis,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.891412Z

Source-reported events for the cited work

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Observation 34a861e6-1005-41f3-822f-8f62cbf0ba4d · outbound

This paper cites A comprehensive survey on graph neural networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning A comprehensive survey on graph neural networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.860208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b86714fb-7c4a-4f2a-be69-38a95b840e0c · outbound

This paper cites Smoothing adversarial training for gnn,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Smoothing adversarial training for gnn,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.831966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e5ed0954-c2a7-4e15-b408-1622b4d1ea98 · outbound

This paper cites Cost-sensitive gnn-based imbalanced learning for mobile social network fraud detection,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Cost-sensitive gnn-based imbalanced learning for mobile social network fraud detection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.813593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dfe138cf-2370-4553-b715-58f5ce59a84b · outbound

This paper cites Label-dependent graph neural network,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Label-dependent graph neural network,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.792788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a5076e60-ca9a-46b7-a293-50d3d8fdb1de · outbound

This paper cites Wiener graph deconvolutional network improves graph self-supervised learning,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Wiener graph deconvolutional network improves graph self-supervised learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.766007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7410ddad-ff25-448a-b267-606da4303a16 · outbound

This paper cites Pre-training on large-scale heterogeneous graph,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Pre-training on large-scale heterogeneous graph,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.737091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation de15556d-89da-44b3-add8-0ca2fac4836b · outbound

This paper cites Node similarity preserving graph convolutional networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Node similarity preserving graph convolutional networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.708179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5eccd5c6-5fae-4987-93d1-c89d42e82a7c · outbound

This paper cites Generative pretraining from pixels,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Generative pretraining from pixels,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.677134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1ff3ca20-9ede-4e9b-9443-d8c1d51ff391 · outbound

This paper cites Unified language model pre-training for natural language understanding and generation,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Unified language model pre-training for natural language understanding and generation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.645987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1cf92e51-37d4-4fab-8bf8-74a2cc599c2d · outbound

This paper cites Language models are few-shot learners,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Language models are few-shot learners,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.621551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 23628d81-c06b-4c72-8ecc-2009a00ec602 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:26.565697Z

Source-reported events for the cited work

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Observation 71df6824-8edd-4325-80b7-882e5f5a6a9e · outbound

This paper cites Gppt: Graph pre- training and prompt tuning to generalize graph neural networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Gppt: Graph pre- training and prompt tuning to generalize graph neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.577735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7cfddd6d-892b-4367-8975-999322b84822 · outbound

This paper cites Graphprompt: Unifying pre- training and downstream tasks for graph neural networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Graphprompt: Unifying pre- training and downstream tasks for graph neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.546623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 92688ab2-238f-4b1f-8af0-ec01512344b6 · outbound

This paper cites Domain adaptation via prompt learning,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Domain adaptation via prompt learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.509874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a7c8f761-2c10-4a21-82b0-5e1b15e4d1dd · outbound

This paper cites Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.457028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aa26be68-8002-458d-843c-ed5df4262205 · outbound

This paper cites HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:12:28.160167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 71b92466-6752-42e8-b482-93f572e71fd1 · outbound

This paper cites GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:26.736464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0758393-ba5a-4e04-95a3-769a4da28a34 · outbound

This paper cites Prompt tuning for graph neural networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Prompt tuning for graph neural networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.433225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2463f172-f39c-41da-af3c-8101cdc69e75 · outbound

This paper cites Virtual node tuning for few-shot node classification,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Virtual node tuning for few-shot node classification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.402697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e23c5a01-326d-454f-9390-d4b3fb21a945 · outbound

This paper cites Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second- order graph matching,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second- order graph matching,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.370981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8f0f219a-4a6f-4873-ac44-96b40b2cf966 · outbound

This paper cites SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:26.867540Z

Source-reported events for the cited work

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Observation 45159743-0cdf-4b8d-b705-f615ff4fe980 · outbound

This paper cites Protein multimer structure prediction via PPI-guided prompt learning,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Protein multimer structure prediction via PPI-guided prompt learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.338278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 32625de9-69a2-43b4-8840-c8b832fb3140 · outbound

This paper cites XPrompt: Exploring the extreme of prompt tuning,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning XPrompt: Exploring the extreme of prompt tuning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.307659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3091f080-2322-465e-80a7-baa23df120a7 · outbound

This paper cites Towards locality- aware meta-learning of tail node embeddings on networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Towards locality- aware meta-learning of tail node embeddings on networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.266884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9855c759-468d-41f3-aa45-30d270ca7b47 · outbound

This paper cites Universal prompt tuning for graph neural networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Universal prompt tuning for graph neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.217475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8b8189dc-c7b0-4ed3-8036-4796e5cb9a2c · outbound

This paper cites Are sixteen heads really better than one?.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Are sixteen heads really better than one?

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.187797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 13e7a364-5d31-40e6-b5a7-e116b64dfb1f · outbound

This paper cites Network together: Node classification via cross-network deep network embedding,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Network together: Node classification via cross-network deep network embedding,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.146956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7b00c638-5654-4b1d-a817-f4783a975068 · outbound

This paper cites Neighborhood attention networks with adversarial learning for link prediction,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Neighborhood attention networks with adversarial learning for link prediction,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.122394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f40287e8-70eb-429f-877b-f28e5e495e0b · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning The lottery ticket hypothesis: Finding sparse, trainable neural networks,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.085587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0fe88ee4-398f-44aa-b8e8-0dfa98e3d711 · outbound

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

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Semi-Supervised Classification with Graph Convolutional Networks

Reference 34

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Source-reported events for the cited work

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Observation 869c8f8d-b117-4be0-8aa0-10b903ca57af · outbound

This paper cites Graph attention networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Graph attention networks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:29.038687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a8febe66-4804-4985-bd81-9d92c2414ecf · outbound

This paper cites Graph contrastive learning with augmentations,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Graph contrastive learning with augmentations,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.998100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fd27ffdd-6e0f-4b37-9c8c-566dcedbfae4 · outbound

This paper cites Commonsense knowledge base completion with relational graph attention network and pre-trained language model,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Commonsense knowledge base completion with relational graph attention network and pre-trained language model,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.959322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b24ea218-d2f7-4f9a-b4fb-fc109134a153 · outbound

This paper cites Graph neural network with curriculum learning for imbalanced node classification,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Graph neural network with curriculum learning for imbalanced node classification,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.925410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 30306b3f-2ffd-4cf5-857d-a7a4b65cfc23 · outbound

This paper cites Pooling architecture search for graph classification,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Pooling architecture search for graph classification,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.888567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c933ca46-ead0-4d39-a249-5dda7b67d31d · outbound

This paper cites Bring your own view: Graph neural networks for link prediction with personalized subgraph selection,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Bring your own view: Graph neural networks for link prediction with personalized subgraph selection,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.859706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 21303d98-5802-44fd-81f1-73bb8438e47a · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:27.378942Z

Source-reported events for the cited work

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Observation 28e6e16f-6832-49aa-b06f-f8e2e1d0d18a · outbound

This paper cites Learning representations of inactive users: A cross domain approach with graph neural networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Learning representations of inactive users: A cross domain approach with graph neural networks,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.814527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f0a998d2-e0d0-4faf-be7a-e8468e3bd224 · outbound

This paper cites Cross- domain few-shot classification based on lightweight res2net and flexible gnn,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Cross- domain few-shot classification based on lightweight res2net and flexible gnn,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.783155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0fbb52ce-cbaf-4d46-a5fa-750126a203e2 · outbound

This paper cites Does gnn pretraining help molecular representation?.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Does gnn pretraining help molecular representation?

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.747187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f19b2170-14ef-4950-b5bd-75fb257a448f · outbound

This paper cites Robust self-supervised structural graph neural network for social network prediction,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Robust self-supervised structural graph neural network for social network prediction,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.706834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e851c50d-85ff-4384-8946-c47218cedb8e · outbound

This paper cites All in one: Multi-task prompting for graph neural networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning All in one: Multi-task prompting for graph neural networks,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.649311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 058cfdbf-5119-4bab-afa7-5a488ce5d90d · outbound

This paper cites Prodigy: Enabling in-context learning over graphs,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Prodigy: Enabling in-context learning over graphs,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.621570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 11f3b67a-e6db-41fc-a68e-380e3b6c851c · outbound

This paper cites ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:12:28.060637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7ee866a3-64cf-4840-9adc-b6480bba0079 · outbound

This paper cites Heterogeneous graph attention network,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Heterogeneous graph attention network,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.576495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 744504d0-e524-434f-9a25-4ec585e7dfde · outbound

This paper cites Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.539902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f9e2ca2a-f856-443a-856b-179ed72bb2bb · outbound

This paper cites Freebase: a collaboratively created graph database for structuring human knowledge,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Freebase: a collaboratively created graph database for structuring human knowledge,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.488809Z

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Observation c6f2a50e-5070-4c5d-be51-798b2497d993 · outbound

This paper cites Deep Graph Infomax.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Deep Graph Infomax

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:27.713644Z

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Observation 85413eaf-4551-4379-ab83-51fbc12623d2 · outbound

This paper cites Graph contrastive learning automated,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Graph contrastive learning automated,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.429874Z

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Observation b5f2f73f-54aa-4718-9c04-10684f4db4f3 · outbound

This paper cites Contrastive pre-training of gnns on heterogeneous graphs,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Contrastive pre-training of gnns on heterogeneous graphs,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.372358Z

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Observation d16aa4a9-c56a-431e-ab95-c70cdba96a5b · outbound

This paper cites Self-supervised heterogeneous graph neural network with co-contrastive learning,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Self-supervised heterogeneous graph neural network with co-contrastive learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.312480Z

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Observation 4eb39084-ce2c-4eeb-871b-d4be0f1fcc27 · outbound

This paper cites Graph few- shot learning with attribute matching,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Graph few- shot learning with attribute matching,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.266674Z

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Observation 487cfac6-7c28-424d-ab96-3750adfc5445 · outbound

This paper cites Graph Prompt Learning: A Comprehensive Survey and Beyond.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Graph Prompt Learning: A Comprehensive Survey and Beyond

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:27.890230Z

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Observation 23c99f5f-af1b-4016-a533-b439264d45c6 · outbound

This paper cites Relative and absolute location embedding for few-shot node classification on graph,.

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning Relative and absolute location embedding for few-shot node classification on graph,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:12:28.218594Z

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Pith citing papers

Observation e9792199-862a-46af-a4af-c4c622aea379 · inbound

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts cites this paper.

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:18:59.773246Z

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Observation e1865b20-c6df-4498-bb79-e2003cede353 · inbound

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts cites this paper.

CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:55:47.463462Z

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