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Paper Citation Record · LEDGER

Influence Maximization with Bandits

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1503.00024.

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

pith.paper-citation-record.v1
1503.00024 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:45:51.320267Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T21:36:17.894239Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b6597414-f24d-4d37-9692-0501639f9d68 · inbound

Offline Learning for Combinatorial Multi-armed Bandits cites this paper.

Offline Learning for Combinatorial Multi-armed Bandits Influence Maximization with Bandits

Reference 2005

Resolution
unresolved
no resolver link, observed 2026-08-09T20:45:51.320267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T20:45:51.320267Z digest=sha256:e9970e8eb430e71ccda20b2946026440235c352a479a5714265dd38c750efb58

Observation 54b10f7a-13f3-4826-98a1-af8cb5832d65 · inbound

Revealing graph bandits for maximizing local influence cites this paper.

Revealing graph bandits for maximizing local influence Influence Maximization with Bandits

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:59:22.603689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-09T20:32:48.082112Z digest=sha256:4a271bec1e3c50eb90ae6d6029b7f5aef8c4a91feec3bc443d221b36163aebb8

Observation 11980f18-1330-4d99-84f6-2158f70f24d2 · inbound

Dynamic Treatment on Networks cites this paper.

Dynamic Treatment on Networks Influence Maximization with Bandits

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:59:22.603689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-08T04:44:39.125232Z digest=sha256:96ae7224f396b4e80a5c0fb36bb4c347035108f420e127d22b747b189254aadf