GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks | Paper Citation Record

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

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

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:06:12.737912Z

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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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:06:12.642421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:06:12.450073Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-11T18:06:11.284779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-11T18:06:11.313604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: malformed identifier; no resolver link, observed 2026-08-11T18:06:11.323597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: 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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: 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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: 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 2026-08-14T06:32:32.682623+00:00.

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: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: verified fuzzy; 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 2026-08-14T06:32:32.682623+00:00.

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

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

Source-reported events for the cited work

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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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: 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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: 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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-14T06:32:32.682623+00:00.

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

Resolution: 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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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

Resolution: 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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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 2026-08-14T06:32:32.682623+00:00.

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.

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