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Paper Citation Record · LEDGER

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks

As of 15 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 1 inbound Pith citation observation for arXiv:2412.08296.

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

pith.paper-citation-record.v1
2412.08296 v3

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:06:11.534062Z

measured 75 of 75 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:35:39.673707Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T05:35:46.633490Z

Reference resolution

74 of 74 outbound references displayed

  • verified exact1
  • verified fuzzy61
  • unresolved11
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 347d616f-f788-4fe9-b0cc-e4a82aa4ae1f · outbound

This paper cites A Survey on Mobile Edge Computing: The Communication Perspective,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks A Survey on Mobile Edge Computing: The Communication Perspective,

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.754127Z

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.

source=pdf_text observed=2026-08-11T18:06:11.139921Z digest=sha256:a578d031d4e252d170e79e592112807762fd1e728911ded06ad8e0269ba5a69b

Observation 19661f69-469d-4c36-82e3-d779b15f2e9d · outbound

This paper cites Mobile Edge Computing: A Survey on Archi- tecture and Computation Offloading,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Mobile Edge Computing: A Survey on Archi- tecture and Computation Offloading,

Reference 2

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raw_fallback, observed 2026-08-11T18:06:12.737912Z

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.

source=pdf_text observed=2026-08-11T18:06:11.148112Z digest=sha256:d80023731e5630f1fe04991f476feb3e0972776de92602280ee06d423c482bb4

Observation 43352607-9b04-4b32-989d-beb4290f9322 · outbound

This paper cites A2-UA V: Application-Aware Content and Network Optimization of Edge-Assisted UA V Systems,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks A2-UA V: Application-Aware Content and Network Optimization of Edge-Assisted UA V Systems,

Reference 3

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raw_fallback, observed 2026-08-11T18:06:12.722164Z

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.

source=pdf_text observed=2026-08-11T18:06:11.154474Z digest=sha256:7471f0a96a0e6c45fa7eedced48c1611b2c324f702680d414d04bef3c00603b4

Observation 02396253-01b1-45dc-904d-97f2c279e252 · outbound

This paper cites Energy-Efficient Trajectory Optimization for Aerial Video Surveillance under QoS Constraints,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Energy-Efficient Trajectory Optimization for Aerial Video Surveillance under QoS Constraints,

Reference 4

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raw_fallback, observed 2026-08-11T18:06:12.705328Z

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.

source=pdf_text observed=2026-08-11T18:06:11.160636Z digest=sha256:407a1b201ca8cbef71410cdf94a1a265321c2461888eb2f0b6e16918b04fadda

Observation 0e70993d-ec7c-49fe-a005-6b2b19054ec3 · outbound

This paper cites Multi-UA V Trajectory and Power Opti- mization for Cached UA V Wireless Networks With Energy and Content Recharging-Demand Driven Deep Learning Approach,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Multi-UA V Trajectory and Power Opti- mization for Cached UA V Wireless Networks With Energy and Content Recharging-Demand Driven Deep Learning Approach,

Reference 5

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raw_fallback, observed 2026-08-11T18:06:12.689862Z

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.

source=pdf_text observed=2026-08-11T18:06:11.166696Z digest=sha256:78af6001b3f2f991c8ae534b7147f5bc4862de9f703186d84ff8ede29b28e9b1

Observation 586b7e91-8ad5-41a1-83db-5e8235799245 · outbound

This paper cites Multi- UA V Trajectory Planning for Energy-Efficient Content Coverage: A Decentralized Learning-Based Approach,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Multi- UA V Trajectory Planning for Energy-Efficient Content Coverage: A Decentralized Learning-Based Approach,

Reference 6

Resolution
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raw_fallback, observed 2026-08-11T18:06:12.674661Z

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.

source=pdf_text observed=2026-08-11T18:06:11.172403Z digest=sha256:deb03e423d57b03d49f9dbb5506406c892c4640851b7ff379a586c81585fb13b

Observation 75e0b59f-3ac7-4d3f-b347-be017d6af158 · outbound

This paper cites Offloading Optimization in Edge Computing for Deep-Learning-Enabled Target Tracking by Internet of UA Vs,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Offloading Optimization in Edge Computing for Deep-Learning-Enabled Target Tracking by Internet of UA Vs,

Reference 7

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raw_fallback, observed 2026-08-11T18:06:12.658764Z

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.

source=pdf_text observed=2026-08-11T18:06:11.178353Z digest=sha256:fbd167ac96dc7238cf085d1df84ebf80a31a6fd86c3a48b1c1c8ebb0e6942d88

Observation b2946910-0b48-49bf-ae9a-147be5dcb7db · outbound

This paper cites Masaracchia, K.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Masaracchia, K

Reference 8

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verified exact
doi, observed 2026-08-11T18:06:11.579406Z

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.

source=pdf_text observed=2026-08-11T18:06:11.183531Z digest=sha256:ead072cee5e4b76eafeaee820185ed5e448a4c5ce9800c97e3bcb4ec4017c049

Observation 5db6a764-5ea4-4c33-b440-793300e00faa · outbound

This paper cites Generative AI-Augmented Graph Reinforcement Learning for Adaptive UA V Swarm Optimization,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Generative AI-Augmented Graph Reinforcement Learning for Adaptive UA V Swarm Optimization,

Reference 9

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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.

source=pdf_text observed=2026-08-11T18:06:11.189499Z digest=sha256:43c73ff829fc442d8342c3dd4baabf6a0698feda1ac4ded351e2168d5b66d2c5

Observation 0a27e393-89e5-4351-9352-abc2d5585294 · outbound

This paper cites Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing,

Reference 10

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raw_fallback, observed 2026-08-11T18:06:12.626836Z

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.

source=pdf_text observed=2026-08-11T18:06:11.194659Z digest=sha256:2bcc6c0c0283bcde71af3cce5e9f8729f55b6d571766edcafeae193941d797a8

Observation 21debbf9-74d8-44e3-a2dc-57a2b2a0eba4 · outbound

This paper cites Federated Edge Network Utility Maximization for a Multi-Server System: Algorithm and Convergence,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Federated Edge Network Utility Maximization for a Multi-Server System: Algorithm and Convergence,

Reference 11

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raw_fallback, observed 2026-08-11T18:06:12.610726Z

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.

source=pdf_text observed=2026-08-11T18:06:11.199578Z digest=sha256:3e30ef01149e0bda4fe33ed39931b8a5e424383da76bed0b50dcda18dbf332c6

Observation 17abe104-1e0b-4567-898d-af436638469d · outbound

This paper cites Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks,

Reference 12

Resolution
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raw_fallback, observed 2026-08-11T18:06:12.595089Z

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.

source=pdf_text observed=2026-08-11T18:06:11.204790Z digest=sha256:17c638e1bbd941f22ed9cedf00ae79209f274bb2ec260497d9b0787387557112

Observation 45250b2b-8663-4352-84e5-b6f85077152e · outbound

This paper cites Reconfigurable Intelligent Surface-Assisted Aerial-Terrestrial Communications via Multi-Task Learning,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Reconfigurable Intelligent Surface-Assisted Aerial-Terrestrial Communications via Multi-Task Learning,

Reference 13

Resolution
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raw_fallback, observed 2026-08-11T18:06:12.578385Z

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.

source=pdf_text observed=2026-08-11T18:06:11.210151Z digest=sha256:23c892261b19acb2e4d8bd257bad68f801d86dfb2060f6bd4b3c4c6b8993829e

Observation 81acc3a4-819b-48e6-a552-7e2f3729d6b5 · outbound

This paper cites Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-User Communications: Achievable Rates With Limited Discrete Phase Shifts,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-User Communications: Achievable Rates With Limited Discrete Phase Shifts,

Reference 14

Resolution
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raw_fallback, observed 2026-08-11T18:06:12.562492Z

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.

source=pdf_text observed=2026-08-11T18:06:11.215709Z digest=sha256:2a507a56cf61e7eea31677db4df6c2f5e2ec940891694105aeb61ed630b3eb23

Observation d5208138-a918-4704-a7c8-3851d455ea17 · outbound

This paper cites Resource Allocation for Power Minimization in RIS-Assisted Multi- UA V Networks With NOMA,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Resource Allocation for Power Minimization in RIS-Assisted Multi- UA V Networks With NOMA,

Reference 15

Resolution
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raw_fallback, observed 2026-08-11T18:06:12.546676Z

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.

source=pdf_text observed=2026-08-11T18:06:11.220653Z digest=sha256:dec6a8335822d63a670fd7b35260bf90c630ba27049ee97f76613f55d838309b

Observation 38879695-e7d5-4c54-93cb-145e77d28c07 · outbound

This paper cites Joint Base Station and IRS Deployment for En- hancing Network Coverage: A Graph-Based Modeling and Optimization Approach,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Joint Base Station and IRS Deployment for En- hancing Network Coverage: A Graph-Based Modeling and Optimization Approach,

Reference 16

Resolution
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raw_fallback, observed 2026-08-11T18:06:12.530851Z

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.

source=pdf_text observed=2026-08-11T18:06:11.225622Z digest=sha256:faf5601dbded25a973bafc729b264714be18c37ab26f0fa0ee7b562d221e6799

Observation 5bcfa31c-5cbd-4a3f-99bc-3feb436fa2bc · outbound

This paper cites Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks: Design Optimization and Analysis,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks: Design Optimization and Analysis,

Reference 17

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raw_fallback, observed 2026-08-11T18:06:12.515031Z

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.

source=pdf_text observed=2026-08-11T18:06:11.230685Z digest=sha256:82816d14c3a0e15b1ba65dfbb0553d02b33d22d524fcb002c63efaa2bdef2af7

Observation 53fb5595-1836-4768-943e-688d369916d0 · outbound

This paper cites Computation Offloading in MEC-Enabled IoV Networks: Average Energy Efficiency Analysis and Learning-Based Maximization,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Computation Offloading in MEC-Enabled IoV Networks: Average Energy Efficiency Analysis and Learning-Based Maximization,

Reference 18

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raw_fallback, observed 2026-08-11T18:06:12.498993Z

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.

source=pdf_text observed=2026-08-11T18:06:11.235543Z digest=sha256:ddfce0996d176f2456a638682795007cb68c323c1f9e5c5e7dec943a7574029a

Observation e9db2ad0-e65d-4367-86d2-41cb59b5e5c8 · outbound

This paper cites Asynchronous Deep Reinforcement Learning for Data-Driven Task Offloading in MEC- Empowered Vehicular Networks,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Asynchronous Deep Reinforcement Learning for Data-Driven Task Offloading in MEC- Empowered Vehicular Networks,

Reference 19

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raw_fallback, observed 2026-08-11T18:06:12.482963Z

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.

source=pdf_text observed=2026-08-11T18:06:11.240647Z digest=sha256:55e0ac7f9dc7d9b8a167fa763840e03c51ae56acc6882a2b896cc0f78778ef82

Observation 3f2ba4e3-7593-4ef3-9775-83da0aa47f7e · outbound

This paper cites Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions,

Reference 20

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raw_fallback, observed 2026-08-11T18:06:12.466987Z

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.

source=pdf_text observed=2026-08-11T18:06:11.245712Z digest=sha256:c4bea9d09bc6f8acb8b44c518c4bb02b1f06b5938d3dde5f75eb6f77de9d77ee

Observation 0a48209d-ada8-4eca-812b-1901ff2f9a0a · outbound

This paper cites DeepScheduler: Enabling Flow-Aware Scheduling in Time-Sensitive Networking,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks DeepScheduler: Enabling Flow-Aware Scheduling in Time-Sensitive Networking,

Reference 21

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raw_fallback, observed 2026-08-11T18:06:12.450073Z

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.

source=pdf_text observed=2026-08-11T18:06:11.250827Z digest=sha256:7942ff28c6362f02894cd782ec854e2aedcf6f711353d0bee367bdcc8d87632d

Observation fe1a1e63-e3b6-47aa-88fe-3cb023a6ace6 · outbound

This paper cites RouteNet: Leveraging graph neural networks for network modeling and optimization in SDN,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks RouteNet: Leveraging graph neural networks for network modeling and optimization in SDN,

Reference 22

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raw_fallback, observed 2026-08-11T18:06:12.431887Z

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.

source=pdf_text observed=2026-08-11T18:06:11.256457Z digest=sha256:6c1cd3c2f179317788bf24eecd59ad4ba5c293f31f4cf6359106972d075e3798

Observation 8886a072-7da6-401b-bc8b-359a606e8029 · outbound

This paper cites A Joint Energy and Latency Framework for Transfer Learning Over 5G Industrial Edge Networks,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks A Joint Energy and Latency Framework for Transfer Learning Over 5G Industrial Edge Networks,

Reference 23

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raw_fallback, observed 2026-08-11T18:06:12.415575Z

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.

source=pdf_text observed=2026-08-11T18:06:11.261949Z digest=sha256:ee5824cacda8c6f26d283f6e0a94c24a14f98586a1544a27fe2193b914f23999

Observation 0c851a64-2227-42cc-9cff-9d2149f9c410 · outbound

This paper cites Deep- learning-based joint resource scheduling algorithms for hybrid MEC networks,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Deep- learning-based joint resource scheduling algorithms for hybrid MEC networks,

Reference 24

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raw_fallback, observed 2026-08-11T18:06:12.399134Z

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.

source=pdf_text observed=2026-08-11T18:06:11.267039Z digest=sha256:4b9ba2cb032ea56a9466b970db92969f82237a976dd57e63a2be990ac70e794e

Observation 347ce20a-9fd5-4d53-bde3-7a3b00c4555d · outbound

This paper cites A multi-head ensemble multi-task learning approach for dynamical com- putation offloading,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks A multi-head ensemble multi-task learning approach for dynamical com- putation offloading,

Reference 25

Resolution
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raw_fallback, observed 2026-08-11T18:06:12.383073Z

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.

source=pdf_text observed=2026-08-11T18:06:11.274013Z digest=sha256:233b0f214d0bca49cf595ce736ff272423d1e4dce795b9059cd8ab52bc57fcb5

Observation 61f2828d-0f36-4a5d-ba4a-f403cfc559a7 · outbound

This paper cites GNN-Based Power Allocation and User Association in Digital Twin Network for the Terahertz Band,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks GNN-Based Power Allocation and User Association in Digital Twin Network for the Terahertz Band,

Reference 26

Resolution
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raw_fallback, observed 2026-08-11T18:06:12.366116Z

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.

source=pdf_text observed=2026-08-11T18:06:11.279513Z digest=sha256:b118d2fc5fb8e01e8b90d03bb2a6c197bbfec6dc95952232e8badca00f7cb30a

Observation 36673dd7-cea4-43f4-b5f1-533acbef9286 · outbound

This paper cites Edge-Assisted Multi-Layer Offloading Optimization of LEO Satellite-Terrestrial Integrated Networks,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Edge-Assisted Multi-Layer Offloading Optimization of LEO Satellite-Terrestrial Integrated Networks,

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.284779Z digest=sha256:e007c45d4383d66d28476d889a4bf76a3b328d044755a4781f974e55b787016f

Observation b560e5a7-f5a0-4800-9b30-123f04bac64b · outbound

This paper cites STaR: self-taught reasoner bootstrapping reasoning with reasoning,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks STaR: self-taught reasoner bootstrapping reasoning with reasoning,

Reference 28

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raw_fallback, observed 2026-08-11T18:06:12.338498Z

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.

source=pdf_text observed=2026-08-11T18:06:11.290624Z digest=sha256:5f599236d8790da403e9ea53dca6b5eeb7d98f2ab284bf4cd3360fc637005434

Observation 54425b33-d128-40ac-a8c4-b6119e38da15 · outbound

This paper cites LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery,

Reference 29

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raw_fallback, observed 2026-08-11T18:06:12.321604Z

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.

source=pdf_text observed=2026-08-11T18:06:11.296320Z digest=sha256:d9a57fdf32d6359d602d6f84c506cc8cf379265be9dee741c8b504edcdac5d13

Observation f76596dd-6ba6-43f3-9ff9-effe5c7afd83 · outbound

This paper cites Dream the impossible: outlier imag- ination with diffusion models,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Dream the impossible: outlier imag- ination with diffusion models,

Reference 30

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raw_fallback, observed 2026-08-11T18:06:12.305029Z

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.

source=pdf_text observed=2026-08-11T18:06:11.302654Z digest=sha256:aafce30158b98d510654e17a8293fb1877b969073528077346f0892aaa6e8b7c

Observation aeb1f5a6-4410-4dff-808f-98c1ea12a60b · outbound

This paper cites Adding Conditional Control to Text-to-Image Diffusion Models,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Adding Conditional Control to Text-to-Image Diffusion Models,

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.288963Z

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.

source=pdf_text observed=2026-08-11T18:06:11.308513Z digest=sha256:805d2139e2ac7f313377c82c97338b39d3b3a3c5b0add73b01013a86358fd701

Observation d519abb0-1ebe-437a-b6ee-b2bf7a15d309 · outbound

This paper cites Gurobi Optimizer Reference Manual,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Gurobi Optimizer Reference Manual,

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.313604Z digest=sha256:c6e33c656caef407b4d34381ed7f5273e15080201ad03f50d38ba55b68244d5c

Observation 62a701f3-011c-4720-b2e4-02d4ff6855d6 · outbound

This paper cites ApS, The MOSEK optimization toolbox for MATLAB manual.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks ApS, The MOSEK optimization toolbox for MATLAB manual

Reference 33

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no resolver link, observed 2026-08-11T18:06:11.323597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.323597Z digest=sha256:6890fa6238f7f86c8f22232b8c4590885765338ff9ce74d1254a9817042ba83f

Observation 167e1561-bf3a-4070-bd75-bda51e06ad09 · outbound

This paper cites IBM ILOG CPLEX Optimization Studio,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks IBM ILOG CPLEX Optimization Studio,

Reference 34

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raw_fallback, observed 2026-08-11T18:06:12.241684Z

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.

source=pdf_text observed=2026-08-11T18:06:11.328591Z digest=sha256:6a627d68fbe38bbefdd4274d98c235c3b8fb52ce413dbe434704342ed38742a5

Observation 472c5c2a-d770-4f18-8925-ecbcf11f05b7 · outbound

This paper cites GEKKO Optimization Suite,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks GEKKO Optimization Suite,

Reference 35

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raw_fallback, observed 2026-08-11T18:06:12.225701Z

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.

source=pdf_text observed=2026-08-11T18:06:11.334161Z digest=sha256:81891214c82303b31f1778ffae5bf6049d21a926e2b3c94c9d9d618a3b4a104f

Observation be19dd27-6255-4639-81e2-453915c1f1c9 · outbound

This paper cites A Survey on Generative Diffusion Models,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks A Survey on Generative Diffusion Models,

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.209483Z

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.

source=pdf_text observed=2026-08-11T18:06:11.339347Z digest=sha256:d3ca3290cd29c0d270ad8c6495e92a7aaeaf4bbd0953020f3e28902d29574cdb

Observation 186ab736-76ef-4c63-b5cb-a13837b3fade · outbound

This paper cites A GNN- based supervised learning framework for resource allocation in wireless IoT networks,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks A GNN- based supervised learning framework for resource allocation in wireless IoT networks,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.193452Z

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.

source=pdf_text observed=2026-08-11T18:06:11.344345Z digest=sha256:b776380a65872184950138115d6ad254ea1486157b37c31a8992381d43faf79c

Observation 58b24eab-b23c-4e1d-ba9c-5a555f398afc · outbound

This paper cites Computation offloading in multi-access edge computing: A multi-task learning approach,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Computation offloading in multi-access edge computing: A multi-task learning approach,

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.177975Z

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.

source=pdf_text observed=2026-08-11T18:06:11.350177Z digest=sha256:a0a1017342086aba474341a8bdec265aab6303f82843f9cc264a97625e5106ca

Observation ee7a93bc-b823-4768-b90a-3700eab155a2 · outbound

This paper cites Denoising Diffusion Probabilistic Models,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Denoising Diffusion Probabilistic Models,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.160720Z

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.

source=pdf_text observed=2026-08-11T18:06:11.355258Z digest=sha256:303d4861c1474613114294fc7b58407d92a081e37b6121db8f8d55ca39446721

Observation 48a8798a-e30f-4644-ba1a-8b95f35afde1 · outbound

This paper cites Classifier-Free Diffusion Guidance.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Classifier-Free Diffusion Guidance

Reference 40

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no resolver link, observed 2026-08-11T18:06:11.360519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.360519Z digest=sha256:b05fc0620f451c257a8865aee913dcdb355f23d73408befe8ca828b912a81a94

Observation 14469c13-ad88-40d0-9aab-bd89c86d801d · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Diffusion Models Beat GANs on Image Synthesis,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.143053Z

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.

source=pdf_text observed=2026-08-11T18:06:11.366152Z digest=sha256:17ffe5c05ccda81937c27e81783730bb1e79a9f14233c813eccfdc04f6965025

Observation 256d001f-0587-4fa8-8bf2-eff0573bf88d · outbound

This paper cites Structured Denoising Diffusion Models in Discrete State-Spaces,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Structured Denoising Diffusion Models in Discrete State-Spaces,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.126764Z

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.

source=pdf_text observed=2026-08-11T18:06:11.371233Z digest=sha256:4b430f2d88690221bb197a16ee6080fc078deef9075bc6dbedbcf018d7df26ac

Observation 7b57c72a-32e6-4078-92af-0164ae852daa · outbound

This paper cites DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization,

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.110842Z

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.

source=pdf_text observed=2026-08-11T18:06:11.376317Z digest=sha256:701cd0e48aeb2811a4ef97a90d4bf53b9a57081fd0407f0012f519025fbc2180

Observation ce8ce676-40d2-410a-bff2-7d35b87fe604 · outbound

This paper cites T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization,

Reference 44

Resolution
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raw_fallback, observed 2026-08-11T18:06:12.094684Z

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.

source=pdf_text observed=2026-08-11T18:06:11.381334Z digest=sha256:c6e6f616606c471a19464686e494e34f2d2934388db564a6788f4d21378b757c

Observation 068beda2-a40a-4ca8-aa80-ef40b82db251 · outbound

This paper cites Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization,

Reference 45

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raw_fallback, observed 2026-08-11T18:06:12.077801Z

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.

source=pdf_text observed=2026-08-11T18:06:11.386740Z digest=sha256:8b4d2f65efc511d8317efd7b81230a52b862327de93e04f089e475337a9ef289

Observation 3940a259-0e73-40b7-ac57-35b352200696 · outbound

This paper cites DiffSG: A Generative Solver for Network Optimization with Diffusion Model.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks DiffSG: A Generative Solver for Network Optimization with Diffusion Model

Reference 46

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no resolver link, observed 2026-08-11T18:06:11.392497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.392497Z digest=sha256:0e8789f1124914983644726d843d25702ce5d9348a063dea6da4e2c89552428c

Observation add9eab8-2c0f-4b0e-b785-4ed3a829678c · outbound

This paper cites Generative AI based Secure Wireless Sensing for ISAC Networks.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Generative AI based Secure Wireless Sensing for ISAC Networks

Reference 47

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no resolver link, observed 2026-08-11T18:06:11.398671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.398671Z digest=sha256:c398f430b20529595ebdc03138f9e67874f2a2ba07eb4f995b0ec1fda959b186

Observation 14b8c700-501c-4c10-9bed-1a92b3b400dd · outbound

This paper cites Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

Reference 48

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no resolver link, observed 2026-08-11T18:06:11.404203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.404203Z digest=sha256:1b3fdf3a89a22d79a55074ae9a2338760fb3df38a600240fe273716dbc668e97

Observation 218eedea-f922-4ae9-aa2f-362971c9a6b5 · outbound

This paper cites Diffusion-Based Reinforcement Learning for Edge-Enabled AI-Generated Content Services,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Diffusion-Based Reinforcement Learning for Edge-Enabled AI-Generated Content Services,

Reference 49

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no resolver link, observed 2026-08-11T18:06:11.409959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.409959Z digest=sha256:9bcafcacf05b6d03e40aa443431fd7b5727fffb55903e287145c18232b76a10e

Observation 410de967-aea7-4c1a-9efd-3aea22c24fda · outbound

This paper cites Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.050762Z

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.

source=pdf_text observed=2026-08-11T18:06:11.415129Z digest=sha256:b399556d0c8fbea78edd50e6eb6b8b3ee776b9b7aa73bdc4ff8774ddc0f2bf00

Observation b8741e30-2f68-415b-bbe8-ff5f5e70cb02 · outbound

This paper cites ADMM for Mobile Edge Intelligence: A Survey,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks ADMM for Mobile Edge Intelligence: A Survey,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.033455Z

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.

source=pdf_text observed=2026-08-11T18:06:11.420454Z digest=sha256:d4bf7dfcf58c3d8062acdf8b96dcb1481d90369fd0bae2638af82690e1cf51ff

Observation 701044c5-ebd2-4461-a282-0aebdc5e73f5 · outbound

This paper cites Online Distributed ADMM Algorithm With RLS-Based Multitask Graph Filter Models,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Online Distributed ADMM Algorithm With RLS-Based Multitask Graph Filter Models,

Reference 52

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verified fuzzy
raw_fallback, observed 2026-08-11T18:06:12.014224Z

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.

source=pdf_text observed=2026-08-11T18:06:11.425349Z digest=sha256:53c683263287c1b44a6ce7b7b3ef10bd03020999e1e36797dd8b81266e54a6b4

Observation f341f903-952f-42b1-bbb0-2896ea40d936 · outbound

This paper cites Hybridized MA- DRL for Serving xURLLC With Cognizable RIS and UA V Integration,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Hybridized MA- DRL for Serving xURLLC With Cognizable RIS and UA V Integration,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.995040Z

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.

source=pdf_text observed=2026-08-11T18:06:11.430334Z digest=sha256:9302c04be99e00aadee7070128b9ae2038ddb89ea1979ee32a83e718f3f6ea71

Observation 8984d529-51ae-4998-8a2f-1f3377468354 · outbound

This paper cites A survey on uplink resource allocation in OFDMA wireless networks,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks A survey on uplink resource allocation in OFDMA wireless networks,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.978473Z

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.

source=pdf_text observed=2026-08-11T18:06:11.435008Z digest=sha256:bcac4130213b03d1590a4b5f802950d1c1a597319800f8f786dac3a6acc4bf02

Observation 02f42dc4-6459-45e4-af00-3208854eb4df · outbound

This paper cites An overview of radio resource management in relay-enhanced OFDMA-based networks,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks An overview of radio resource management in relay-enhanced OFDMA-based networks,

Reference 55

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verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.961759Z

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.

source=pdf_text observed=2026-08-11T18:06:11.440150Z digest=sha256:2ae77085df0eeff075517445cc20feb1f396dd06376a0d51321b163bd032c61e

Observation 290be0a6-2495-4d62-8d84-c869d2842211 · outbound

This paper cites Decentralized computation offloading game for mobile cloud computing,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Decentralized computation offloading game for mobile cloud computing,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.944974Z

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.

source=pdf_text observed=2026-08-11T18:06:11.445528Z digest=sha256:1930ccabcc395d4a2bc2b421f50bf27e25476377b2ea356e7ace301a7c9f6f11

Observation 0ea4454d-a8ea-4954-8de8-3b1a7b9e719b · outbound

This paper cites Processor design for portable systems,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Processor design for portable systems,

Reference 57

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unresolved
no resolver link, observed 2026-08-11T18:06:11.450333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.450333Z digest=sha256:c182c4a7344908909097be61c3fad63ea3dfdbf2425fa89a77059bcce8494723

Observation 567c4320-0a14-4734-91b2-9c05953c13b7 · outbound

This paper cites DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.916644Z

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.

source=pdf_text observed=2026-08-11T18:06:11.455985Z digest=sha256:cd795799384a10a8f8f0be4530627cd645cd2412266e07b64e1c2a55833b2126

Observation 3f3be26f-740d-408f-b136-d6bb63eb463d · outbound

This paper cites Chebyshev inequality with estimated mean and variance,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Chebyshev inequality with estimated mean and variance,

Reference 59

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verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.899873Z

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.

source=pdf_text observed=2026-08-11T18:06:11.461181Z digest=sha256:a69e2a67ee9d289a410c045574330d6d5833c5b8ba56ae2d7d3bd8f8a047b7e8

Observation d80303a3-196e-49b0-8cc6-e5b9a7f2757b · outbound

This paper cites Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.884018Z

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.

source=pdf_text observed=2026-08-11T18:06:11.467177Z digest=sha256:b8e200436c81fe5d81848974c05313da0513bf7059194ccbf0165ad5fa32c48c

Observation 86d4eeed-2f54-40ed-8731-5422a408888c · outbound

This paper cites Variational Diffusion Models,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Variational Diffusion Models,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.867004Z

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.

source=pdf_text observed=2026-08-11T18:06:11.472305Z digest=sha256:b06afca741a35f9c85fe25fc3e3133c7086f413f87ca6bc85bac85ce7c122312

Observation 98e3771b-dd1a-4943-bb8b-292c4f1d6973 · outbound

This paper cites Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.850425Z

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.

source=pdf_text observed=2026-08-11T18:06:11.477164Z digest=sha256:3b6f5ced17ef120f4172672390180bbf396641df02a1f0e5663ac69c1b5d5cb5

Observation 01636d89-6222-4275-844b-968f3e177d34 · outbound

This paper cites Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 63

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unresolved
no resolver link, observed 2026-08-11T18:06:11.483467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.483467Z digest=sha256:78b4dd4365a2d22e3d1dfde559d9d16e00f18aa23d69b9ac2c220f65ae0c8dd5

Observation f4e60458-8c98-4d31-8485-66d87e3f65d0 · outbound

This paper cites Learning the travelling salesperson problem requires rethinking generalization,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Learning the travelling salesperson problem requires rethinking generalization,

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-11T18:06:11.488843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.488843Z digest=sha256:c747ad35cb8ce3bb09a0c29890582022bf92c95459cd78eda4280d0d5905ac53

Observation 3bb6e2cd-6869-4725-b09f-415b1acd110c · outbound

This paper cites Benchmarking graph neural networks,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Benchmarking graph neural networks,

Reference 65

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verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.822527Z

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.

source=pdf_text observed=2026-08-11T18:06:11.493631Z digest=sha256:0a5068f8c209845f2144e681b6594382633f9d0c1a7c1cd9d73ebd51350dff46

Observation f5254911-c912-417a-a5d7-4566c2bb4c5d · outbound

This paper cites A systematic survey on deep generative models for graph generation,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks A systematic survey on deep generative models for graph generation,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.806003Z

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.

source=pdf_text observed=2026-08-11T18:06:11.498546Z digest=sha256:b56c9262c2c07f9513ab7e3ce758cd9b5dfd289756ecf9ef77fc359b2dc048b3

Observation b8f729a4-9f88-455c-930e-7043a608e3c1 · outbound

This paper cites Towards Impartial Multi-task Learning,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Towards Impartial Multi-task Learning,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.790090Z

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.

source=pdf_text observed=2026-08-11T18:06:11.503957Z digest=sha256:69e6d7e263a243e36db92ba0cd0d244e33139143b2d20cacfb967061d762a76b

Observation 5d1a0c42-b1b1-4f33-8158-4253f1ee6121 · outbound

This paper cites Gradient Surgery for Multi-Task Learning,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Gradient Surgery for Multi-Task Learning,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.772266Z

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.

source=pdf_text observed=2026-08-11T18:06:11.509196Z digest=sha256:72c8d9857e9918ebe1412709af78040eb8d94d1538ce1e64f3ac05f892606809

Observation 0e59b083-543e-468d-a18d-8569aefb143f · outbound

This paper cites Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.754650Z

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.

source=pdf_text observed=2026-08-11T18:06:11.513893Z digest=sha256:0adba7b2be7e950a505adfbd5cdcf368ebdc8c25f3234aa681756ab0f79d864c

Observation d65dc6e8-9eae-458c-a38b-7c42a83fd298 · outbound

This paper cites Addressing Negative Transfer in Diffusion Models,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Addressing Negative Transfer in Diffusion Models,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.738227Z

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.

source=pdf_text observed=2026-08-11T18:06:11.518747Z digest=sha256:d9e42ced9ca9d387c8d5ce5371864d0af0d832fb2a87cb197f249325ae4d5d10

Observation 90833026-0695-4ece-bd3e-fc8dc1716160 · outbound

This paper cites DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.721296Z

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.

source=pdf_text observed=2026-08-11T18:06:11.523891Z digest=sha256:20b362652b3cca64d85fd3b26c4d3aaa641878eff62d786df491af879426c65d

Observation 1bf532ba-7483-4a75-baca-a742bfc0d68a · outbound

This paper cites Diffusion Model is an Effective Planner and Data Synthesizer for Multi- Task Reinforcement Learning,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Diffusion Model is an Effective Planner and Data Synthesizer for Multi- Task Reinforcement Learning,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.700277Z

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.

source=pdf_text observed=2026-08-11T18:06:11.529099Z digest=sha256:cac4aba6e2654b3f7ba26669b752af24e10cdeea7ab8b4f64f96367714309a10

Observation c4599839-0757-47b1-8035-7d647da2e513 · outbound

This paper cites Denoising Diffusion Implicit Models,.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Denoising Diffusion Implicit Models,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:06:11.682978Z

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.

source=pdf_text observed=2026-08-11T18:06:11.534062Z digest=sha256:3447f6a6137a439d9aab45de7f89efc3d797cab894bbf365478b7e038165146d

Observation 20d3bfbb-b047-412e-88d2-62c1fb668243 · outbound

This paper cites Available: https://www.gurobi.com.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Available: https://www.gurobi.com

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-11T18:06:11.318600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.318600Z digest=sha256:d76b6c1bed973d6c9eabdb94186dc04d76fe224f85eec04218bcf4314a90ff70

Pith citing papers

Observation 311f662d-a1fb-437e-9868-5b651e8baea7 · inbound

Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences cites this paper.

Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks

Reference 169

Resolution
verified exact
local_arxiv, observed 2026-08-06T05:35:46.710939Z

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.

source=pdf_text observed=2026-08-06T05:35:39.673707Z digest=sha256:846f2609ddcc9a6dff533c3970f281234ca7a486d96ef8bebfea709b7bb5ad46