Paper Citation Record · LEDGER

TopoFormer: Topology Meets Attention for Graph Learning

As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.28259.

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

pith.paper-citation-record.v1
2607.28259 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T13:03:59.267985Z

measured 30 of 30 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 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

30 of 30 outbound references displayed

verified exact0
verified fuzzy0
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 36e73318-5f73-46df-8f5d-012ec701d401 · outbound

This paper cites Two stability lemmas.

TopoFormer: Topology Meets Attention for Graph Learning Two stability lemmas

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:59.047125Z

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Observation 26ca36ec-c1cb-4e6e-85c1-9dbdab467445 · outbound

This paper cites The outputs z1, z2, and z3 are then combined through a learnable weighted sum.

TopoFormer: Topology Meets Attention for Graph Learning The outputs z1, z2, and z3 are then combined through a learnable weighted sum

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:59.256914Z

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Observation dacfb4ae-66c3-4b19-92ef-d69a157f7452 · outbound

This paper cites For the MLP component, we employed a two-layer MLP with a hidden dimension of 200, ensuring that its output dimension matches the output dimension of the TOPOFORMERmodel.

TopoFormer: Topology Meets Attention for Graph Learning For the MLP component, we employed a two-layer MLP with a hidden dimension of 200, ensuring that its output dimension matches the output dimension of the TOPOFORMERmodel

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.985391Z

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Observation ae620162-f7dc-4c6d-8a4d-c39735248266 · outbound

This paper cites an unresolved cited work.

TopoFormer: Topology Meets Attention for Graph Learning Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:59.267985Z

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Observation 27a4cac5-ab23-477b-8794-672749a6155b · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

TopoFormer: Topology Meets Attention for Graph Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.839231Z

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Observation 97aa699e-a0c1-498a-b544-02091391b933 · outbound

This paper cites Knowledge graph-enhanced molecular contrastive learning with functional prompt.

TopoFormer: Topology Meets Attention for Graph Learning Knowledge graph-enhanced molecular contrastive learning with functional prompt

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.983116Z

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Observation 2c57d62c-eaab-4131-b8e4-d901872c29a2 · outbound

This paper cites Gemnet: Universal directional graph neural networks for molecules.Advances in neural information processing systems, 34:6790–6802,.

TopoFormer: Topology Meets Attention for Graph Learning Gemnet: Universal directional graph neural networks for molecules.Advances in neural information processing systems, 34:6790–6802,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.060063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eaa59e8-b1c0-4c98-9e94-754d07345010 · outbound

This paper cites SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery.

TopoFormer: Topology Meets Attention for Graph Learning SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.134871Z

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Observation 04759d9b-ae74-4d96-aa00-41877c23942c · outbound

This paper cites N-gram graph: Simple unsupervised repre- sentation for graphs, with applications to molecules.Advances in neural information processing systems, 32,.

TopoFormer: Topology Meets Attention for Graph Learning N-gram graph: Simple unsupervised repre- sentation for graphs, with applications to molecules.Advances in neural information processing systems, 32,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.218331Z

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Unavailable: canonical work link unavailable.

Observation 6038cb02-a14f-4af3-a550-dc21983a8b5a · outbound

This paper cites Pre- training molecular graph representation with 3d geometry.

TopoFormer: Topology Meets Attention for Graph Learning Pre- training molecular graph representation with 3d geometry

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.329322Z

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Observation 1ebb8df8-4ffd-4528-b881-db41f2ddb9e4 · outbound

This paper cites Graph alignment kernels using weisfeiler and leman hierarchies.

TopoFormer: Topology Meets Attention for Graph Learning Graph alignment kernels using weisfeiler and leman hierarchies

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.392331Z

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Observation ec75895e-7682-4a1b-831e-b2f3fbef946f · outbound

This paper cites Model-agnostic augmentation for accurate graph classification.

TopoFormer: Topology Meets Attention for Graph Learning Model-agnostic augmentation for accurate graph classification

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.733777Z

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Observation a8a83674-d27f-441f-8051-32b201fefa98 · outbound

This paper cites Benchmarking Large Language Models for Molecule Prediction Tasks.

TopoFormer: Topology Meets Attention for Graph Learning Benchmarking Large Language Models for Molecule Prediction Tasks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.807838Z

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Observation 3d1439fc-ec3f-4990-8dd7-2259c5c0af82 · outbound

This paper cites Proof of Theorem 3.1.By Theorem 3.1, we have∥ bβk(G, f)− bβk(G, g)∥1 ≤C d B(M f k , Mg k ).

TopoFormer: Topology Meets Attention for Graph Learning Proof of Theorem 3.1.By Theorem 3.1, we have∥ bβk(G, f)− bβk(G, g)∥1 ≤C d B(M f k , Mg k )

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:59.113713Z

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Observation 10dd812c-d785-4c85-8f80-fa0619408bd6 · outbound

This paper cites A full expressivity comparison and formal information-loss bounds relative to complete barcodes are interesting directions for future work.

TopoFormer: Topology Meets Attention for Graph Learning A full expressivity comparison and formal information-loss bounds relative to complete barcodes are interesting directions for future work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:59.226480Z

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Observation dbb9ce11-f438-45e3-b2ee-24bf7742d16c · outbound

This paper cites an unresolved cited work.

TopoFormer: Topology Meets Attention for Graph Learning Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:59.260788Z

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Observation fd435613-f503-4e6f-9278-7d31bf209c1b · outbound

This paper cites an unresolved cited work.

TopoFormer: Topology Meets Attention for Graph Learning Unresolved cited work

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:59.264497Z

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Observation 43b5177b-59bf-4f69-98cb-d0be611cacfa · outbound

This paper cites They encode features such as atomic connectivity and substructural patterns, enabling efficient similarity search and predictive modeling.

TopoFormer: Topology Meets Attention for Graph Learning They encode features such as atomic connectivity and substructural patterns, enabling efficient similarity search and predictive modeling

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.908127Z

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Observation 13c8c139-8db3-494f-9ce5-24c015c38f35 · outbound

This paper cites Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veli ˇckovi´c.

TopoFormer: Topology Meets Attention for Graph Learning Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veli ˇckovi´c

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.549048Z

Source-reported events for the cited work

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Observation d50fe3c5-3adf-45ad-b1af-99a313fad336 · outbound

This paper cites Self-supervised graph transformer on large-scale molecular data.Advances in neural information processing systems, 33:12559–12571,.

TopoFormer: Topology Meets Attention for Graph Learning Self-supervised graph transformer on large-scale molecular data.Advances in neural information processing systems, 33:12559–12571,

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.471544Z

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Observation f83e0b33-2095-4bdd-9305-06a1f9fe08c1 · outbound

This paper cites Topogcl: Topological graph contrastive learning.

TopoFormer: Topology Meets Attention for Graph Learning Topogcl: Topological graph contrastive learning

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.467766Z

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Observation 1a9afb99-1617-4407-b484-c15c43281efe · outbound

This paper cites Persistence homology of networks: methods and applications.Applied Network Science, 4(1):1–28,.

TopoFormer: Topology Meets Attention for Graph Learning Persistence homology of networks: methods and applications.Applied Network Science, 4(1):1–28,

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.102684Z

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Observation 8f6c591b-c469-4f51-a2ea-57790bffb2ca · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

TopoFormer: Topology Meets Attention for Graph Learning Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.320195Z

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Observation f83b6bfd-d236-4c63-9180-20215016f354 · outbound

This paper cites A Survey of Vectorization Methods in Topological Data Analysis.

TopoFormer: Topology Meets Attention for Graph Learning A Survey of Vectorization Methods in Topological Data Analysis

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.180296Z

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Observation fe88b643-8279-4b1a-a692-27778b178665 · outbound

This paper cites Topological Methods in Machine Learning: A Tutorial for Practitioners.

TopoFormer: Topology Meets Attention for Graph Learning Topological Methods in Machine Learning: A Tutorial for Practitioners

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.649989Z

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Observation 468d2223-3f77-4827-a6e1-53cb2fa0fe3a · outbound

This paper cites Moleculenet: a benchmark for molecular machine learning.

TopoFormer: Topology Meets Attention for Graph Learning Moleculenet: a benchmark for molecular machine learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.652971Z

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Observation 07895a90-a6fa-4741-9bb8-f9632435a00c · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

TopoFormer: Topology Meets Attention for Graph Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.769769Z

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Observation de461a17-c40a-429e-bad5-bce6072b383e · outbound

This paper cites Understanding the Power of Persistence Pairing via Permutation Test.

TopoFormer: Topology Meets Attention for Graph Learning Understanding the Power of Persistence Pairing via Permutation Test

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.391927Z

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Observation 44281ec1-6e16-47ce-89b2-4c0072140766 · outbound

This paper cites Ripsnet: a general architecture for fast and robust estimation of the persistent homology of point clouds.

TopoFormer: Topology Meets Attention for Graph Learning Ripsnet: a general architecture for fast and robust estimation of the persistent homology of point clouds

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:57.712185Z

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Observation f0b29cb3-7a0f-4663-824b-e55d60145314 · outbound

This paper cites Communicative representation learning on attributed molecular graphs.

TopoFormer: Topology Meets Attention for Graph Learning Communicative representation learning on attributed molecular graphs

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-07-31T13:03:58.576970Z

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Pith citing papers

No inbound Pith citation observations are available.