Paper Citation Record · LEDGER

Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search

As of 22 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.

pith.paper-citation-record.v1
2608.08406 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:41:26.082966Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+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 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.