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researcher

Chanyoung Park

35 papers hereh-index 263k citations57 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author10
  • last author18

Across the 34 of 35 papers where every author was matched, so the position is known.

fields
  • cs.LG20
  • cs.IR7
  • cs.CL2
  • cs.SI2
  • cs.AI1
  • cs.CE1
same name
  • Chanyoung Park — 34 papers, h 9
  • Chanyoung Park — 15 papers, h 5
  • Chanyoung Park — 11 papers, h 3
  • Chanyoung Park — 9 papers, h 2
  • Chanyoung Park — 2 papers, h 12
  • Chanyoung Park — 2 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172026
most citedHDMI: High-order Deep Multiplex Infomax

156 citations · 420 across the 33 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.LG2021

Augmentation-Free Self-Supervised Learning on Graphs

Namkyeong Lee, Junseok Lee, Chanyoung Park

Inspired by the recent success of self-supervised methods applied on images, self-supervised learning on graph structured data has seen rapid growth especially centered on augmenta…

cs.LG2021★ 3 cited

Learnable Structural Semantic Readout for Graph Classification

Dongha Lee, Su Kim, Seonghyeon Lee +2

With the great success of deep learning in various domains, graph neural networks (GNNs) also become a dominant approach to graph classification. By the help of a global readout op…

cs.LG2021

Bootstrapping User and Item Representations for One-Class Collaborative Filtering

Dongha Lee, SeongKu Kang, Hyunjun Ju +2

The goal of one-class collaborative filtering (OCCF) is to identify the user-item pairs that are positively-related but have not been interacted yet, where only a small portion of…

cs.LG2021★ 156 cited

HDMI: High-order Deep Multiplex Infomax

Baoyu Jing, Chanyoung Park, Hanghang Tong

Networks have been widely used to represent the relations between objects such as academic networks and social networks, and learning embedding for networks has thus garnered plent…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.