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K. Chang

21 papers hereh-index 4814.5k citations186 works total

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

author position
  • middle author10
  • last author11

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CL10
  • cs.LG5
  • cs.SI3
  • cs.DB2
  • cs.HC1
same name
  • K. Chang — 21 papers, h 9
  • K. Chang — 14 papers, h 16
  • K. Chang — 6 papers, h 22
  • K. Chang — 6 papers, h 2
  • K. Chang — 6 papers, h 4
  • K. Chang — 6 papers, h 3

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
20122023
most citedGeom-GCN: Geometric Graph Convolutional Networks

119 citations · 189 across the 15 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.LG2020★ 4 cited

Curvature Regularization to Prevent Distortion in Graph Embedding

Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang +2

Recent research on graph embedding has achieved success in various applications. Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding spac…

cs.CL2020

Exploring Semantic Capacity of Terms

Jie Huang, Zilong Wang, Kevin Chen-Chuan Chang +2

We introduce and study semantic capacity of terms. For example, the semantic capacity of artificial intelligence is higher than that of linear regression since artificial intellige…

cs.LG2020★ 1 cited

Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model

Chaochao Chen, Kevin C. Chang, Qibing Li +1

Recently latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values…

cs.LG2020★ 119 cited

Geom-GCN: Geometric Graph Convolutional Networks

Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang +2

Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weakne…

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