Paper Citation Record · LEDGER

Effective Capacitance Modeling Using Graph Neural Networks

As of 4 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.03787.

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

pith.paper-citation-record.v1
2507.03787 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T05:44:07.545084Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+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

29 of 29 outbound references displayed

verified exact4
verified fuzzy23
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d6af140a-4b37-4db9-bf95-a58be9c36d05 · outbound

This paper cites an unresolved cited work.

Effective Capacitance Modeling Using Graph Neural Networks Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-19T06:23:00.392737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7a2e4498-8455-4940-acfb-5dca68fabe3d · outbound

This paper cites an unresolved cited work.

Effective Capacitance Modeling Using Graph Neural Networks Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-05-19T06:23:00.347404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 03e83944-5c14-44ea-86f6-088fbcad0465 · outbound

This paper cites Available from https://www.synopsys.com.

Effective Capacitance Modeling Using Graph Neural Networks Available from https://www.synopsys.com

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.355831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d50521a9-784d-430a-9b85-c17b56eea14d · outbound

This paper cites Performance computation for precharacterized CMOS gates with RC loads.

Effective Capacitance Modeling Using Graph Neural Networks Performance computation for precharacterized CMOS gates with RC loads

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.367947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 04f968be-fe16-4e1e-9957-d778326e961d · outbound

This paper cites Modeling the driving-point characteristic of resistive interconnect for accurate delay estimation.

Effective Capacitance Modeling Using Graph Neural Networks Modeling the driving-point characteristic of resistive interconnect for accurate delay estimation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.343341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 910bd3e1-dac3-4cb4-869d-b9529d947026 · outbound

This paper cites Modeling the.

Effective Capacitance Modeling Using Graph Neural Networks Modeling the

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.390134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 403dbc3b-9e99-441b-9620-2d6f0ed23b8e · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

Effective Capacitance Modeling Using Graph Neural Networks Semi-supervised classification with graph convolutional networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.362259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation bdaf5eb3-4942-4cd7-a510-6065fb4b72ed · outbound

This paper cites Graph Attention Networks.

Effective Capacitance Modeling Using Graph Neural Networks Graph Attention Networks

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-19T05:47:08.073934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 671ea416-caf3-488e-9d41-6531a06e9d62 · outbound

This paper cites How Attentive are Graph Attention Networks?.

Effective Capacitance Modeling Using Graph Neural Networks How Attentive are Graph Attention Networks?

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-19T05:47:08.070694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b5bcf742-f891-4320-b791-5f7ef908d84a · outbound

This paper cites Inductive Representation Learning on Large Graphs.

Effective Capacitance Modeling Using Graph Neural Networks Inductive Representation Learning on Large Graphs

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-19T05:47:08.066223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 57a2efcb-3ab3-49eb-9c72-75e8aeb34fa3 · outbound

This paper cites Attention Is All You Need.

Effective Capacitance Modeling Using Graph Neural Networks Attention Is All You Need

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-19T05:47:08.077035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 915ac10f-7b7e-493a-abb0-be2c086045a4 · outbound

This paper cites Graph matching networks for learning the similarity of graph structured objects.

Effective Capacitance Modeling Using Graph Neural Networks Graph matching networks for learning the similarity of graph structured objects

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.397587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f122ec40-6560-4fbb-8135-938cde32c72c · outbound

This paper cites An optimization- aware pre-routing timing prediction framework based on heterogeneous graph learning.

Effective Capacitance Modeling Using Graph Neural Networks An optimization- aware pre-routing timing prediction framework based on heterogeneous graph learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.383868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7fabd159-48d1-4d8e-bdea-d26557862a4a · outbound

This paper cites From global route to detailed route: Ml for fast and accurate wire parasitics and timing prediction.

Effective Capacitance Modeling Using Graph Neural Networks From global route to detailed route: Ml for fast and accurate wire parasitics and timing prediction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.386054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a2480a5c-63ae-46e4-a3a6-38efa0476029 · outbound

This paper cites Accurate timing path delay learning using feature enhancer with effective capacitance.

Effective Capacitance Modeling Using Graph Neural Networks Accurate timing path delay learning using feature enhancer with effective capacitance

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.394965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2a33e4c6-6831-4159-8213-32e018a519cd · outbound

This paper cites Rc-gnn: Fast and accurate signoff wire delay estimation with customized graph neural networks.

Effective Capacitance Modeling Using Graph Neural Networks Rc-gnn: Fast and accurate signoff wire delay estimation with customized graph neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.345519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 044a5fd7-77bd-4efc-9911-924117575d37 · outbound

This paper cites Synctree: Fast timing analysis for integrated circuit design through a physics-informed tree-based graph neural network.

Effective Capacitance Modeling Using Graph Neural Networks Synctree: Fast timing analysis for integrated circuit design through a physics-informed tree-based graph neural network

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.378041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c1a29aa0-f756-4b7d-ad24-3fb8bc173ec2 · outbound

This paper cites Paragraph: Layout parasitics and device parameter prediction using graph neural networks.

Effective Capacitance Modeling Using Graph Neural Networks Paragraph: Layout parasitics and device parameter prediction using graph neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.373457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b903fe0a-f3cf-46e1-ac92-bdcb8cce6b9b · outbound

This paper cites Deep-learning- based pre-layout parasitic capacitance prediction on sram designs.

Effective Capacitance Modeling Using Graph Neural Networks Deep-learning- based pre-layout parasitic capacitance prediction on sram designs

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.360321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6a727250-ea04-4af9-83be-7e08284493ee · outbound

This paper cites OpenROAD: Toward a self-driving, open-source digital layout implementation tool chain.

Effective Capacitance Modeling Using Graph Neural Networks OpenROAD: Toward a self-driving, open-source digital layout implementation tool chain

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.365905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation bd6a9e04-5064-4d4a-a78d-0c2d50684f18 · outbound

This paper cites Openlane-flow-scripts.

Effective Capacitance Modeling Using Graph Neural Networks Openlane-flow-scripts

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.379998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5091c987-169c-47f8-967b-afd94faeec06 · outbound

This paper cites ASAP7: A 7-nm finFET predictive process design kit.

Effective Capacitance Modeling Using Graph Neural Networks ASAP7: A 7-nm finFET predictive process design kit

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.382065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3bb5316c-c77f-45c4-9dd2-7e9ecbcab1cf · outbound

This paper cites OpenSTA: Static timing analyzer.

Effective Capacitance Modeling Using Graph Neural Networks OpenSTA: Static timing analyzer

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.353893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 0c3d0c1a-697d-425c-a13a-811cb5c17c05 · outbound

This paper cites Openrcx.

Effective Capacitance Modeling Using Graph Neural Networks Openrcx

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.351581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 0da2121b-aa19-4cd9-b05a-ce068ebb12d4 · outbound

This paper cites Ngspice users manual version 39.

Effective Capacitance Modeling Using Graph Neural Networks Ngspice users manual version 39

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.364055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e8dd7250-3574-489a-89ad-c6acd2efe7f8 · outbound

This paper cites The GeoSteiner software package for computing Steiner trees in the plane: an updated computational study.

Effective Capacitance Modeling Using Graph Neural Networks The GeoSteiner software package for computing Steiner trees in the plane: an updated computational study

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.358087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c7654b99-6567-438c-857c-46eeeddfff01 · outbound

This paper cites Tune: A research platform for distributed model selection and training.

Effective Capacitance Modeling Using Graph Neural Networks Tune: A research platform for distributed model selection and training

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.388051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 18b4df4e-5d54-4b24-99c1-2d3a079c9d28 · outbound

This paper cites PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation.

Effective Capacitance Modeling Using Graph Neural Networks PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.370444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation bc8d66c0-b892-48b5-b469-71962b4b3e8d · outbound

This paper cites Fast graph representation learning with PyTorch Geometric.

Effective Capacitance Modeling Using Graph Neural Networks Fast graph representation learning with PyTorch Geometric

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T06:23:00.349563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

No inbound Pith citation observations are available.