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Paper Citation Record · LEDGER

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2602.23135.

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

pith.paper-citation-record.v1
2602.23135 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:30:53.298773Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

21 of 21 outbound references displayed

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External citation measurements

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Outbound references

Observation 74f2df86-8a32-4421-8931-2d3933f23794 · outbound

This paper cites Dvgmae: Self- supervised dynamic variational graph masked autoen- coder.IEEE Transactions on Neural Networks and Learn- ing Systems,.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Dvgmae: Self- supervised dynamic variational graph masked autoen- coder.IEEE Transactions on Neural Networks and Learn- ing Systems,

Reference 1

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Observation bdaf186a-7216-426e-8746-75aff6b5c262 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Representation Learning with Contrastive Predictive Coding

Reference 6

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Observation 19069a9a-0cac-43ce-85d1-2efb8e41ac8d · outbound

This paper cites Towards better evaluation for dynamic link prediction.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Towards better evaluation for dynamic link prediction

Reference 8

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Observation eef54c8b-3f0f-4fc6-ad0d-78ceba552211 · outbound

This paper cites Dysat: Deep neural rep- resentation learning on dynamic graphs via self-attention networks.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Dysat: Deep neural rep- resentation learning on dynamic graphs via self-attention networks

Reference 10

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Observation f10d943e-2e92-4f60-9927-db3d9ca6b452 · outbound

This paper cites Dyrep: Learn- ing representations over dynamic graphs.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Dyrep: Learn- ing representations over dynamic graphs

Reference 11

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Observation e3e9df9a-f966-405a-a1ee-2a765f5edeb9 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 12

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Observation df5e5110-af83-4328-89f9-05987bfbf15d · outbound

This paper cites Inductive Representation Learning on Temporal Graphs.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Inductive Representation Learning on Temporal Graphs

Reference 15

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Observation caa9612d-261d-4d7e-9b3c-6f51652ac3a3 · outbound

This paper cites Cldg: Con- trastive learning on dynamic graphs.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Cldg: Con- trastive learning on dynamic graphs

Reference 16

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Observation c2875186-f5f0-4441-9cca-c50b20eec58e · outbound

This paper cites Dtgb: A comprehensive benchmark for dynamic text-attributed graphs.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Dtgb: A comprehensive benchmark for dynamic text-attributed graphs

Reference 18

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Observation 2f0bd719-957b-490c-829a-9f058720ff18 · outbound

This paper cites The underlying raw data originates from publicly available sources, cited below.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs The underlying raw data originates from publicly available sources, cited below

Reference 20

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Observation 3c436e87-f765-485e-9a82-8b0247445094 · outbound

This paper cites GDELT4 is built from the Global Database of Events, Lan- guage, and Tone, recording international political events.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs GDELT4 is built from the Global Database of Events, Lan- guage, and Tone, recording international political events

Reference 2002

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Observation 24e326bf-31ae-40c2-a7eb-723c7e8df012 · outbound

This paper cites Tinybert: Distilling bert for natural language under- standing.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Tinybert: Distilling bert for natural language under- standing

Reference 2016

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Observation 9a673202-878b-408c-a3e4-7d1f552c4462 · outbound

This paper cites TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

Reference 2017

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Observation 72386b53-f454-46d8-a8a2-cf4b161bbdf8 · outbound

This paper cites Schardl, and Charles E.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Schardl, and Charles E

Reference 2018

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Observation 983b33fc-147f-4e9b-a733-7d5057f21841 · outbound

This paper cites [Liuet al., 2025 ] Weixiong Liu, Junwei Cheng, Quanlong Guan, Zhongyu Pan, and Chaobo He.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs [Liuet al., 2025 ] Weixiong Liu, Junwei Cheng, Quanlong Guan, Zhongyu Pan, and Chaobo He

Reference 2019

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Observation 3b963974-1a71-42ba-9174-bd0bae0b365b · outbound

This paper cites Predicting dynamic embedding trajectory in temporal interaction networks.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Predicting dynamic embedding trajectory in temporal interaction networks

Reference 2020

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Observation 30861bbc-9f26-4d42-b1c9-29251b479073 · outbound

This paper cites Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks

Reference 2021

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Observation 6e44826a-7b42-4086-8641-558258a18ecf · outbound

This paper cites Temporal Graph Networks for Deep Learning on Dynamic Graphs.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 2022

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Observation 8049befc-d7e1-4790-8563-c87563d7e5c3 · outbound

This paper cites Towards better dynamic graph learning: New archi- tecture and unified library.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Towards better dynamic graph learning: New archi- tecture and unified library

Reference 2023

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Observation 279c62cd-b8bc-41bd-8ce9-127107083aed · outbound

This paper cites Topology-monitorable contrastive learning on dynamic graphs.

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Topology-monitorable contrastive learning on dynamic graphs

Reference 2024

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Observation 8ed8787a-ce39-4088-b205-ba9b11cc053b · outbound

This paper cites Gaussian Error Linear Units (GELUs).

DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs Gaussian Error Linear Units (GELUs)

Reference 2025

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Pith citing papers

No inbound Pith citation observations are available.