Paper Citation Record · LEDGER

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication

As of 12 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2508.20077.

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

pith.paper-citation-record.v1
2508.20077 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:17:15.226177Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

verified exact10
verified fuzzy2
unresolved9
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c8bedb25-8819-47c8-8eff-62b031b4e102 · outbound

This paper cites Delay and disruption tolerant networking for terrestrial and TCP/IP applications: A systematic literature review,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Delay and disruption tolerant networking for terrestrial and TCP/IP applications: A systematic literature review,

Reference 1

Resolution: verified exact; doi, observed 2026-08-05T15:17:15.326757Z

Source-reported events for the cited work

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Observation 74e2fcaf-128b-4615-86c2-e052389c8f4f · outbound

This paper cites Development of delay-tolerant networking protocols for reliable data transmission in space networks: A simulation-based approach,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Development of delay-tolerant networking protocols for reliable data transmission in space networks: A simulation-based approach,

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:17.369896Z

Source-reported events for the cited work

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Observation bfd0925c-641e-4723-aaac-672b1c87f43a · outbound

This paper cites Challenges and opportunities to enhance buffer management for disaster area in delay tolerant network: A review with performance analysis,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Challenges and opportunities to enhance buffer management for disaster area in delay tolerant network: A review with performance analysis,

Reference 3

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Source-reported events for the cited work

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Observation 246dc4a4-a5fb-4693-8c90-214f640585f6 · outbound

This paper cites IPS: Integrating pose with speech for enhancement of body pose estimation in VR remote collaboration,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication IPS: Integrating pose with speech for enhancement of body pose estimation in VR remote collaboration,

Reference 4

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Source-reported events for the cited work

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Observation f2673f60-dcb0-4864-8fea-4b7daf839a47 · outbound

This paper cites A multi -attribute-based data forwarding scheme for delay tolerant networks,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication A multi -attribute-based data forwarding scheme for delay tolerant networks,

Reference 5

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Source-reported events for the cited work

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Observation cdf8f122-49cb-4972-9f61-6ded71fb936d · outbound

This paper cites Optimal routing with machine learning classification in delay tolerant networks,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Optimal routing with machine learning classification in delay tolerant networks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T15:17:15.099398Z

Source-reported events for the cited work

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Observation 5e595514-e3f2-494f-b759-2404cde78e78 · outbound

This paper cites A novel cross -layer framework for large scale emergency communications,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication A novel cross -layer framework for large scale emergency communications,

Reference 7

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:17.031221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7c803826-316e-4309-903d-996e24320b87 · outbound

This paper cites An optimized load balancing probabilistic protocol for delay tolerant networks,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication An optimized load balancing probabilistic protocol for delay tolerant networks,

Reference 8

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Source-reported events for the cited work

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Observation 86da79e7-0191-45be-a00b-4bb55787b4e0 · outbound

This paper cites Machine learning based intelligent routing for VDTNs,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Machine learning based intelligent routing for VDTNs,

Reference 9

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:17:16.952881Z

Source-reported events for the cited work

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Observation 4495cfd8-fc93-4a82-b00c-7ecce1778378 · outbound

This paper cites A review of applicable technologies, routing protocols, requirements, and architecture for disaster area networks,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication A review of applicable technologies, routing protocols, requirements, and architecture for disaster area networks,

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:16.864996Z

Source-reported events for the cited work

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Observation cf5eedd0-cd17-4daa-abae-f2cea5fce406 · outbound

This paper cites Coupled electromagnetic-thermal analysis of a dry-type power transformer,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Coupled electromagnetic-thermal analysis of a dry-type power transformer,

Reference 11

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:17:16.781675Z

Source-reported events for the cited work

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Observation a4f0be9f-443b-472c-a872-b019acf3b61a · outbound

This paper cites A survey on machine learning techniques for routing optimization in SDN,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication A survey on machine learning techniques for routing optimization in SDN,

Reference 12

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:16.715443Z

Source-reported events for the cited work

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Observation bdc65989-aebb-4af9-977e-2d7563df1a74 · outbound

This paper cites Time-series flexible resampling for continuous and real -time finger character recognition,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Time-series flexible resampling for continuous and real -time finger character recognition,

Reference 13

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Source-reported events for the cited work

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Observation ca0708ce-9513-4716-97b9-7b9b668101ff · outbound

This paper cites Applications of machine learning in networking: A survey of current issues and future challenges,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Applications of machine learning in networking: A survey of current issues and future challenges,

Reference 14

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:16.547499Z

Source-reported events for the cited work

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Observation 7f9e93bd-4313-404a-8d66-be3246a9d123 · outbound

This paper cites Cognitive Caching at the Edges for Mobile Social Community Networks: A Multi -Agent Deep Reinforcement Learning Approac h,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Cognitive Caching at the Edges for Mobile Social Community Networks: A Multi -Agent Deep Reinforcement Learning Approac h,

Reference 15

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:16.467182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8540f98e-4207-4ac2-90d6-eb06be64cfce · outbound

This paper cites PECCS 2019: 34-45.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication PECCS 2019: 34-45

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:17:17.396644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 61f69c6e-59c6-43cb-b5c3-bc8fa0f4f21d · outbound

This paper cites Elastically accelerating lookup on virtual SDN flow tables for software -defined cloud gateways,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Elastically accelerating lookup on virtual SDN flow tables for software -defined cloud gateways,

Reference 17

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:16.371330Z

Source-reported events for the cited work

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Observation 4b65340d-a786-4ff8-b491-82301c379edb · outbound

This paper cites Extending sparse tensor accelerators to support multiple compression formats,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Extending sparse tensor accelerators to support multiple compression formats,

Reference 18

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Observation 7024b507-4475-4049-914a-e297e8e3e2f1 · outbound

This paper cites Comparison of machine learning techniques applied to traffic prediction of real wireless network,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Comparison of machine learning techniques applied to traffic prediction of real wireless network,

Reference 19

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:16.234572Z

Source-reported events for the cited work

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Observation 2b838e59-0a05-4cd1-b30a-e07f16208d09 · outbound

This paper cites Multi -similarity fusion- based label propagation for predicting microbes potentially associated with diseases,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Multi -similarity fusion- based label propagation for predicting microbes potentially associated with diseases,

Reference 20

Resolution: verified exact; doi, observed 2026-08-05T15:17:15.304308Z

Source-reported events for the cited work

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Observation 009488de-11d3-4f14-87fb-f6f9e623c916 · outbound

This paper cites Energy-efficient locomotion generation and theoretical analysis of a quasi -passive dynamic walker,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Energy-efficient locomotion generation and theoretical analysis of a quasi -passive dynamic walker,

Reference 21

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Observation d0f7c4f5-8727-49b7-80c6-b204baea08ee · outbound

This paper cites Enhancing DTN rout ing strategies with deep reinforcement learning in disaster recovery networks,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Enhancing DTN rout ing strategies with deep reinforcement learning in disaster recovery networks,

Reference 22

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Source-reported events for the cited work

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Observation 65bc13c3-e457-43c8-9ca3-b99833f272cf · outbound

This paper cites Recent Advances in Disaster Emergency Response Planning: Integrating Optimization, Machine Learning, and Simulation.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Recent Advances in Disaster Emergency Response Planning: Integrating Optimization, Machine Learning, and Simulation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:17:15.294124Z

Source-reported events for the cited work

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Observation d0f5fb20-9f47-49da-9936-ff1d390f3eb1 · outbound

This paper cites Design considerations of implementing process bus and PTP along an IEC 61850-based main line traction power system,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Design considerations of implementing process bus and PTP along an IEC 61850-based main line traction power system,

Reference 24

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:17:15.968940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d8f97e9f-45b6-4fa4-9c22-4aec33b8a2d4 · outbound

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ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Mobility Context Aware Routing Protocol in DTN,

Reference 25

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:17:15.888841Z

Source-reported events for the cited work

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Observation e6ec9f37-6e54-4c2d-bea7-13f7b0ea4274 · outbound

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ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Traffic prediction in SDN for explainable QoS using deep learning approach,

Reference 26

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This paper cites Weisfeiler and Leman Go Neural: Higher -Order Graph Neural Networks,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Weisfeiler and Leman Go Neural: Higher -Order Graph Neural Networks,

Reference 27

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Source-reported events for the cited work

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ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Performance Evaluation of DTN Routing Protocols on Map -Based Social Mobility Models for DTN Networks,

Reference 28

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Source-reported events for the cited work

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Observation a8e6c565-7b0c-49c4-b912-a8b97098b658 · outbound

This paper cites Improved Multistage Continuous -Time Pipelined Analog-to-Digital Converters and the Implicit Decimation Property,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Improved Multistage Continuous -Time Pipelined Analog-to-Digital Converters and the Implicit Decimation Property,

Reference 29

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:15.808841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 41a99785-855b-4a07-a87a-7a03d7e41bdb · outbound

This paper cites Enhancing Emergency Communication for Future Smart Cities with Random Forest Model.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Enhancing Emergency Communication for Future Smart Cities with Random Forest Model

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:17:15.729902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9201b9f4-6e1a-4921-b52d-2d1aae831837 · outbound

This paper cites Vehicular mobility monitoring using remote sensing and deep learning on a UAV-based mobile computing platform,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Vehicular mobility monitoring using remote sensing and deep learning on a UAV-based mobile computing platform,

Reference 31

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:15.714064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 78860723-76cb-476a-a697-9d442ac70066 · outbound

This paper cites B5GEMINI: AI-Driven Network Digital Twin,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication B5GEMINI: AI-Driven Network Digital Twin,

Reference 32

Resolution: verified exact; doi, observed 2026-08-05T15:17:15.259455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c0a8648a-c5e9-4ecd-8e3f-0d7f63b42568 · outbound

This paper cites Sizing Optimization of Whale Optimization Algorithm for Hybrid Stand -Alone Photovoltaic System,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Sizing Optimization of Whale Optimization Algorithm for Hybrid Stand -Alone Photovoltaic System,

Reference 33

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:17:15.638193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 87cff174-2a06-42ae-833e-b90c58acc884 · outbound

This paper cites Adaptive Real -Time Predictive Collaborative Content Discovery and Retrieval in Mobile Disconnection Prone Networks,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Adaptive Real -Time Predictive Collaborative Content Discovery and Retrieval in Mobile Disconnection Prone Networks,

Reference 34

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:15.565021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 791f99d6-bf51-4926-a2d9-7da6aa8f0202 · outbound

This paper cites Towards Low Cost Prototyping of Mobile Opportunistic Disconnection Tolerant Networks and Systems,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Towards Low Cost Prototyping of Mobile Opportunistic Disconnection Tolerant Networks and Systems,

Reference 35

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:15.482275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3409efd9-0a7a-4666-874f-f38c23b07633 · outbound

This paper cites Enabling Real -time Communications and Services in Heterogeneous Networks of Drones and Vehicles,.

ML-MaxProp: Bridging Machine Learning and Delay-Tolerant Routing for Resilient Post-Disaster Communication Enabling Real -time Communications and Services in Heterogeneous Networks of Drones and Vehicles,

Reference 36

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:17:15.398661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.