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Paper Citation Record · LEDGER

DISCO: Influence Maximization Meets Network Embedding and Deep Learning

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1906.07378.

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

pith.paper-citation-record.v1
1906.07378 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:41:25.853053Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T22:49:32.528899Z

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 365aff03-1196-4053-8a74-3c8eb00fd46a · inbound

REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization cites this paper.

REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization DISCO: Influence Maximization Meets Network Embedding and Deep Learning

Reference 49

Resolution
verified exact
local_arxiv, observed 2026-08-10T22:49:32.538286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-10T22:49:32.211764Z digest=sha256:ea2fcf615ce169f846f30c3448dd5f68505899f19008f6c163b5c2ad718e0ef6

Observation 827fabe1-c8d7-4d34-8a6f-f0f91702fdad · inbound

Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search cites this paper.

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.

source=pdf_text observed=2026-08-14T04:41:25.853053Z digest=sha256:eb85ff7aa4ecfbb0d594e41802ea44488f47e666e7ec58bc9690b08ed6887741