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Paper Citation Record · LEDGER

Sampling on networks: estimating eigenvector centrality on incomplete graphs

As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:1908.00388.

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

pith.paper-citation-record.v1
1908.00388 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:58:38.763542Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

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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

36 of 36 outbound references displayed

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  • unresolved4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f3275f6-94d2-4bc6-b6b1-0721dbfabea9 · outbound

This paper cites We define the border of a sampled subgraph as the set of all the incoming neighbors not include d in the sample itself.

Sampling on networks: estimating eigenvector centrality on incomplete graphs We define the border of a sampled subgraph as the set of all the incoming neighbors not include d in the sample itself

Reference 1

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This paper cites However, these two quantities are difficult to treat analytically and we therefore rely on acti ng on γ, which is instead accessible.

Sampling on networks: estimating eigenvector centrality on incomplete graphs However, these two quantities are difficult to treat analytically and we therefore rely on acti ng on γ, which is instead accessible

Reference 2

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Observation ccabcad9-0ab4-49d3-9cce-e61f9c3634c3 · outbound

This paper cites How does the data sampling strategy impact the discovery of info r- mation diffusion in social media? In F ourth International AAAI Conference on W eblogs and Social Media, 2010.

Sampling on networks: estimating eigenvector centrality on incomplete graphs How does the data sampling strategy impact the discovery of info r- mation diffusion in social media? In F ourth International AAAI Conference on W eblogs and Social Media, 2010

Reference 3

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Observation 98b853f1-d151-4828-aa47-cfcd1ebe1b6d · outbound

This paper cites Correcting for missing data in information cas- cades.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Correcting for missing data in information cas- cades

Reference 4

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Observation a4af1840-b9d9-4250-8d1a-d5f78d79fc99 · outbound

This paper cites Sampling from large graphs.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Sampling from large graphs

Reference 5

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This paper cites Towards compress- ing web graphs.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Towards compress- ing web graphs

Reference 6

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Observation a8fb92f2-8150-4424-9ac6-6bdc3271b1cd · outbound

This paper cites Network sampling and model fitting.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Network sampling and model fitting

Reference 7

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Observation 07515535-b4bc-4e94-9f7c-8c117f86b29e · outbound

This paper cites Effect of sampling on topology predic- tions of protein-protein interaction networks.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Effect of sampling on topology predic- tions of protein-protein interaction networks

Reference 8

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Observation f74bc7bb-d71e-494d-8dd4-372901585e8e · outbound

This paper cites Sta- tistical properties of sampled networks.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Sta- tistical properties of sampled networks

Reference 9

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This paper cites Effects of missing data in social networ ks.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Effects of missing data in social networ ks

Reference 10

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This paper cites Factoring and weighting approaches to st a- tus scores and clique identification.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Factoring and weighting approaches to st a- tus scores and clique identification

Reference 11

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Observation 8dba83c5-9108-4a56-8ca5-d257d946a60c · outbound

This paper cites The stability of centrality measures when networks are sampled.

Sampling on networks: estimating eigenvector centrality on incomplete graphs The stability of centrality measures when networks are sampled

Reference 12

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Unresolved cited work

Reference 13

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This paper cites Empirical com- parison of network sampling: How to choose the most appropri - ate method? Physica A: Statistical Mechanics and its Applica- tions, 477:136–148, 2017.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Empirical com- parison of network sampling: How to choose the most appropri - ate method? Physica A: Statistical Mechanics and its Applica- tions, 477:136–148, 2017

Reference 14

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Observation bc59205f-d711-48b2-b27e-64c0097ea939 · outbound

This paper cites Is the sample good enough? comparing data from twitter’ s streaming api with twitter’s firehose.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Is the sample good enough? comparing data from twitter’ s streaming api with twitter’s firehose

Reference 15

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Sampling on networks: estimating eigenvector centrality on incomplete graphs On unbiased sampling for unstructure d peer-to-peer networks

Reference 16

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Metropolis algorithms for representat ive subgraph sampling

Reference 17

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Observation b050ef4e-9351-4d9a-8324-3164925a3efe · outbound

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Sampling properties of random graphs: the degree distribution

Reference 18

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Estimation of vertex degrees in a sampled network

Reference 19

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This paper cites Sampling-based Estimation of In-degree Distribution with Applications to Directed Complex Networks.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Sampling-based Estimation of In-degree Distribution with Applications to Directed Complex Networks

Reference 20

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Sampling on networks: estimating eigenvector centrality on incomplete graphs The anatomy of a large-scale hypertextual web search engine

Reference 21

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Sampling on networks: estimating eigenvector centrality on incomplete graphs An improved method for efficient pagerank estimation

Reference 22

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Local methods for estimating pagerank values

Reference 23

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Matrix computations, volume 3

Reference 24

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Walking in facebook: A case study of unbiased sampling of osns

Reference 25

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Local estimates for eigenvector-like cent ral- ities of complex networks

Reference 26

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Sampling on networks: estimating eigenvector centrality on incomplete graphs On random graphs, i

Reference 27

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Quantifying the completeness of the observed internet as-l evel structure

Reference 28

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Grap h evolution: Densification and shrinking diameters

Reference 29

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Trust management for the semantic web

Reference 30

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Community structure in large networks: Natural cluster sizes and the absence of large well-defined c lus- ters

Reference 31

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Random walks on graphs: A survey

Reference 32

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Equation of state calculations by fast computing machines

Reference 33

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Snowball sampling

Reference 34

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Sampling on networks: estimating eigenvector centrality on incomplete graphs Sampling community structure

Reference 35

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raw_fallback, observed 2026-08-14T15:58:39.040714Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-14T15:58:38.697478Z digest=sha256:926691aa9fea9cf9e919e637dd2d30d0af09ca3e8ad138c0a0f318b55067cbc1

Observation afd90e40-8341-4411-b478-ffcea8c217b4 · outbound

This paper cites Nonparametric statis- tics: A step-by-step approach.

Sampling on networks: estimating eigenvector centrality on incomplete graphs Nonparametric statis- tics: A step-by-step approach

Reference 36

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raw_fallback, observed 2026-08-14T15:58:39.026980Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:58:38.763542Z digest=sha256:c16f5542b908f033fc564c398f0c742052a73951825457656840eaf5c624b620

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