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Paper Citation Record · LEDGER

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs

As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2505.18300.

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

pith.paper-citation-record.v1
2505.18300 v3

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:47:22.890118Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T23:22:35.875578Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:22:46.537900Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact2
  • verified fuzzy16
  • unresolved12
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3a02bcfb-5d58-460a-888a-9789e5bd213f · outbound

This paper cites an unresolved cited work.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Unresolved cited work

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 4e973093-2c38-4878-967a-847a79476289 · outbound

This paper cites Name # of nodes.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Name # of nodes

Reference 2

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 73f74b98-7e10-4b49-b5db-c1a30a508db0 · outbound

This paper cites an unresolved cited work.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Unresolved cited work

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 716d0e08-d3a9-4255-bca8-3aed17a2502f · outbound

This paper cites an unresolved cited work.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 32888924-1096-4106-96d3-186033192c63 · outbound

This paper cites an unresolved cited work.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Unresolved cited work

Reference 7

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation dbf362bf-c10b-4b76-8eeb-6ae57319f327 · outbound

This paper cites Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e3a3e5d8-6553-423e-a792-a5bab5a4ada8 · outbound

This paper cites N., and Zhang, R.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs N., and Zhang, R

Reference 10

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation fb284262-244a-49a1-b11f-07a67d2b8a7b · outbound

This paper cites Challenging the limits: Sampling online social networks with cost constraints.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Challenging the limits: Sampling online social networks with cost constraints

Reference 13

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Observation 40c7843a-f2eb-458c-aea8-0dd247d20fe1 · outbound

This paper cites Proof of Lemma 3.2 The proof consists of three parts: We first show that V (x) = X i∈X µi(xi/µi)−α (26) is the Lyapunov function of the ODE ˙x = π[x] − x.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Proof of Lemma 3.2 The proof consists of three parts: We first show that V (x) = X i∈X µi(xi/µi)−α (26) is the Lyapunov function of the ODE ˙x = π[x] − x

Reference 16

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Observation f808de21-24de-4051-bfa0-94741f3081c2 · outbound

This paper cites In our Algorithm 1, replacing the target of the base MCMC sampler from µ to π[x] results in an ergodic transition matrix P [x] parameterized by x, which is π[x]-invariant.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs In our Algorithm 1, replacing the target of the base MCMC sampler from µ to π[x] results in an ergodic transition matrix P [x] parameterized by x, which is π[x]-invariant

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d1f6e60b-f0f4-432b-9a3f-46e472855b70 · outbound

This paper cites tX s=1 Φ(θ∗, Zs) #.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs tX s=1 Φ(θ∗, Zs) #

Reference 18

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 48b48edb-2c04-463d-bc7c-37af196016ab · outbound

This paper cites For simulation setting, We run 50 independent trials with 300,000 steps and set one-third of the number of steps as burn-in period before collecting samples.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs For simulation setting, We run 50 independent trials with 300,000 steps and set one-third of the number of steps as burn-in period before collecting samples

Reference 20

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 755ef997-c82c-4770-b565-5d8582635895 · outbound

This paper cites More results on HDT-MCMC in graph sampling In this section, we present more simulation results on the algorithm that utilizes HDT-MCMC framework for graph sampling in other graph.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs More results on HDT-MCMC in graph sampling In this section, we present more simulation results on the algorithm that utilizes HDT-MCMC framework for graph sampling in other graph

Reference 22

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Observation 6e21f810-bbde-44f9-bc94-85c49cdf177a · outbound

This paper cites an unresolved cited work.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Unresolved cited work

Reference 24

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation ea8ae5ae-fd4a-4282-b833-1ec61b9f7545 · outbound

This paper cites Moreover, in p2p-Gnutella08 graph, we notice that the performance of LRU design is robust to the choice of the memory capacity.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Moreover, in p2p-Gnutella08 graph, we notice that the performance of LRU design is robust to the choice of the memory capacity

Reference 25

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 1a328969-7a00-4f15-8cd2-6c1051be49ad · outbound

This paper cites 500 1000 1500 2000 2500 3000 Steps 0.4 0.5 0.6 0.7 0.8 0.9 1.0TVD WikiVote Graph MHRW MHRW-HDT ( = 0.5) MHRW-HDT ( =.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs 500 1000 1500 2000 2500 3000 Steps 0.4 0.5 0.6 0.7 0.8 0.9 1.0TVD WikiVote Graph MHRW MHRW-HDT ( = 0.5) MHRW-HDT ( =

Reference 26

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 1d36ea98-f9ab-4c4a-b4e2-c6a7d3aafc46 · outbound

This paper cites an unresolved cited work.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Unresolved cited work

Reference 28

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Observation 924e201b-7e32-432a-a2c2-6738242c1c04 · outbound

This paper cites an unresolved cited work.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Unresolved cited work

Reference 29

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 88c6f17a-f8cb-41e7-9032-494963c2d525 · outbound

This paper cites µi ∝ di with α = 1 in HDT.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs µi ∝ di with α = 1 in HDT

Reference 30

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 73f7afe6-8bdb-41db-bdcb-8abb81bddadf · outbound

This paper cites and Hwang, C.-R.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs and Hwang, C.-R

Reference 1999

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 1dd23367-952b-4187-a045-545f447367a8 · outbound

This paper cites Irreversible Samplers from Jump and Continuous Markov Processes.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Irreversible Samplers from Jump and Continuous Markov Processes

Reference 2000

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 841ba0c8-5812-4957-96df-6c7a0d0868b4 · outbound

This paper cites Revisiting Random Walks for Learning on Graphs.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Revisiting Random Walks for Learning on Graphs

Reference 2002

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Unavailable: canonical work link unavailable.

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Observation b92ba2e3-de76-45ad-9e30-0db38ec8e9a6 · outbound

This paper cites Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data

Reference 2005

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:47:20.148987Z digest=sha256:26975db204c1f8ffa64df4354b4078a76a86fef14d95f47ed52b107c7ec8b8b6

Observation f6cb2536-060c-44d4-a04e-2486a75bb3c8 · outbound

This paper cites and Livingstone, S.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs and Livingstone, S

Reference 2007

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c6e64264-d29b-4b41-9dc5-47463a87cb87 · outbound

This paper cites Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees

Reference 2010

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no resolver link, observed 2026-08-07T14:47:20.905930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:47:20.905930Z digest=sha256:4139e47edd6d29f2c1c901d18196b8a5f7f5f1be3763e1a2347e3a674c2bc20b

Observation 118bb033-3d2b-4ce6-adf2-3da046b4c1c0 · outbound

This paper cites Given a sequence of random variable θ1, θ2, · · ·with partial sum Sn ≜ Pn s=1 θs such that 1√n Sn n→∞ − − − − → dist.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Given a sequence of random variable θ1, θ2, · · ·with partial sum Sn ≜ Pn s=1 θs such that 1√n Sn n→∞ − − − − → dist

Reference 2013

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raw_fallback, observed 2026-08-07T14:47:26.670909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:47:21.798897Z digest=sha256:316a5af4d31b10541e1555aec00834ae492aaba2e4ec93d7de7ecb85109da360

Observation ed77960a-987a-49b6-894b-c6ebe57c179b · outbound

This paper cites Decentralized learning with random walks and communication-efficient adaptive optimization.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Decentralized learning with random walks and communication-efficient adaptive optimization

Reference 2020

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raw_fallback, observed 2026-08-07T14:47:28.150981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:47:21.001438Z digest=sha256:07bb87da8c63d1c288982485db4e8e43be9652d80ea246ea00d4e42b1dc16005

Observation eb4c6855-2b72-40f0-afb3-f2cce5da54db · outbound

This paper cites Lifting Markov Chains To Mix Faster: Limits and Opportunities.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Lifting Markov Chains To Mix Faster: Limits and Opportunities

Reference 2021

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local_arxiv, observed 2026-08-07T14:47:23.587362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation f399b553-03cb-4d0b-8c0c-2389563c8422 · outbound

This paper cites Matrix Form of Covariance Matrix (4) The multivariate version of Br´emaud (2013, eq.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Matrix Form of Covariance Matrix (4) The multivariate version of Br´emaud (2013, eq

Reference 2022

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raw_fallback, observed 2026-08-07T14:47:27.724908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 33ea65b1-26cc-4c93-875b-3a4a39644406 · outbound

This paper cites Efficient Unbiased Sparsification.

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs Efficient Unbiased Sparsification

Reference 2023

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local_arxiv, observed 2026-08-07T14:47:23.381639Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:47:19.622145Z digest=sha256:f519e3bd2edf1d072ca2ad3a5cab7d320c86506882ef22d6cd33bb2b603ee39c

Pith citing papers

Observation 2fd3cb88-ea04-4cfb-a12b-4babd3f7ab94 · inbound

True Self-Avoiding Walk for Accelerating Markov-Chain Monte Carlo Integration cites this paper.

True Self-Avoiding Walk for Accelerating Markov-Chain Monte Carlo Integration Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs

Reference 2

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arxiv_id, observed 2026-06-28T23:22:46.539577Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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