Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-28T04:04:49.690338Z
Paper Citation Record · LEDGER
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators
As of 20 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2606.04838.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
10 of 10 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved9
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c20cc3dc-3b57-4b10-b3d7-8e445c3adc7d · outbound
This paper cites Key Performance Indicators (KPI) for Evolved Universal Terrestrial Radio Access Network (E-UTRAN).
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators Key Performance Indicators (KPI) for Evolved Universal Terrestrial Radio Access Network (E-UTRAN)
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T04:04:49.690338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation deb0a088-15c4-4997-9745-82a511401206 · outbound
This paper cites Radio measurement collection for Minimization of Drive Tests (MDT); Overall description; Stage 2.
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators Radio measurement collection for Minimization of Drive Tests (MDT); Overall description; Stage 2
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T04:04:49.690338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6cb53982-a74b-40bf-81d4-570a361fbf4d · outbound
This paper cites Autonomous Network Levels Evaluation Methodology.
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators Autonomous Network Levels Evaluation Methodology
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T04:04:49.690338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eeaef88a-d752-4107-bce6-88e6cd799069 · outbound
This paper cites an unresolved cited work.
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators Unresolved cited work
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T04:04:49.690338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4eaac452-1aae-4129-9216-f28691a47211 · outbound
This paper cites , year = 1951, month=aug, Address =.
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators , year = 1951, month=aug, Address =
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T04:04:49.690338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e84e63f7-8019-4bf5-a290-8d6a9e96f28a · outbound
This paper cites Minimizing age of information in vehicular networks , year=.
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators Minimizing age of information in vehicular networks , year=
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T04:04:49.690338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa7c4bb3-3209-4550-9424-546ac95663b8 · outbound
This paper cites XGBoost: A Scalable Tree Boosting System.
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators XGBoost: A Scalable Tree Boosting System
Reference 7
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T11:26:54.084101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ef52bd87-5c4b-43f2-a212-6d6d170f0f3f · outbound
This paper cites and Alammouri, Ahmad and Alkhateeb, Ahmed and Andrews, Jeffrey G.
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators and Alammouri, Ahmad and Alkhateeb, Ahmed and Andrews, Jeffrey G
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T04:04:49.690338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5e928fb-381f-4e4b-a4f6-5fbaa256c6e5 · outbound
This paper cites and Hoydis, Jakob , journal=.
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators and Hoydis, Jakob , journal=
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T04:04:49.690338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 41bc0c68-67aa-488a-8825-30446910cfe4 · outbound
This paper cites 2016 , Address =.
From Network Experience to Subscriber Retention: An Explainable AI Framework for Mobile Operators 2016 , Address =
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T04:04:49.690338Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.