Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T04:41:26.082966Z
Paper Citation Record · LEDGER
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search
As of 19 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2608.08406.
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 2026-08-18T06:34:40.430872+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
14 of 14 outbound references displayed
- verified exact1
- verified fuzzy10
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 2f1c4391-6b2c-43c0-82c1-13a062fa0cbd · outbound
This paper cites An O(m) Algorithm for Cores Decomposition of Networks.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search An O(m) Algorithm for Cores Decomposition of Networks
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T04:41:25.736142Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 182278a7-ac79-40f9-9270-d754229a2a1b · outbound
This paper cites an unresolved cited work.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T04:41:26.622885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8d8f9e8a-4a34-4877-bf01-e1c2fa6abafa · outbound
This paper cites Qintian Guo, Sibo Wang, Zhewei Wei, and Ming Chen.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Qintian Guo, Sibo Wang, Zhewei Wei, and Ming Chen
Reference 1978
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:27.513644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fa8e3ead-78d8-4ee3-97bc-6e6863268c67 · outbound
This paper cites Dynaflux: Implicit dynamics-preserving reinforcement learning for topology-free influence maximization.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Dynaflux: Implicit dynamics-preserving reinforcement learning for topology-free influence maximization
Reference 1998
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:26.984838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 827fabe1-c8d7-4d34-8a6f-f0f91702fdad · outbound
This paper cites DISCO: Influence Maximization Meets Network Embedding and Deep Learning.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search DISCO: Influence Maximization Meets Network Embedding and Deep Learning
Reference 2007
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T04:41:25.853053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 21ba576a-d950-49f2-b510-0976ae245f76 · outbound
This paper cites Online processing algorithms for influence maximization.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Online processing algorithms for influence maximization
Reference 2012
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:27.302776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e680c4b9-49a6-44fe-8cf8-a5c1231e95d5 · outbound
This paper cites Influence maximization in near-linear time: A martingale approach.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Influence maximization in near-linear time: A martingale approach
Reference 2014
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:27.099798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ffc60157-d8b7-4821-943a-02f98a7e2528 · outbound
This paper cites Social Contagion: An Empirical Study of Information Spread on Digg and Twitter Follower Graphs.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Social Contagion: An Empirical Study of Information Spread on Digg and Twitter Follower Graphs
Reference 2015
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T04:41:26.364755Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4d60c367-7430-4868-9263-d936855f1ad6 · outbound
This paper cites All models are trained for 200 epochs using AdamW (Kingma & Ba, 2015; Loshchilov & Hutter,.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search All models are trained for 200 epochs using AdamW (Kingma & Ba, 2015; Loshchilov & Hutter,
Reference 2016
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:26.707100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation eadb6a97-a3fe-42e3-992c-6a7bfe811d9a · outbound
This paper cites Influence maximization: Near-optimal time complexity meets practical efficiency.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Influence maximization: Near-optimal time complexity meets practical efficiency
Reference 2018
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:27.204746Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 221dcee1-6950-4725-bfb7-914040acea85 · outbound
This paper cites Pyg 2.0: Scalable learning on real world graphs.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Pyg 2.0: Scalable learning on real world graphs
Reference 2019
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:27.610841Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b09b6ceb-24e4-4641-895a-4fdcfca393f4 · outbound
This paper cites While these architectural improvements enhance predictive capacity, they also introduce training complexity.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search While these architectural improvements enhance predictive capacity, they also introduce training complexity
Reference 2022
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:26.809413Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 52744ce8-298b-4ab9-b33d-ebaca0b526e5 · outbound
This paper cites Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:27.403151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c382df30-cedc-4824-ac6b-670955a5b6ad · outbound
This paper cites Specifically, it replaces conventional message-passing networks with a sheaf neural network (Hansen & Gebhart, 2020; Bodnar et al.,.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search Specifically, it replaces conventional message-passing networks with a sheaf neural network (Hansen & Gebhart, 2020; Bodnar et al.,
Reference 2025
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T04:41:26.901309Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.