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Lingxiao Zhao

19 papers hereh-index 173.2k citations33 works total

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

author position
  • first author8
  • middle author10
  • last author1

Across the 19 of 19 papers where every author was matched, so the position is known.

fields
  • cs.LG18
  • eess.SP1
same name
  • Lingxiao Zhao — 22 papers, h 17
  • Lingxiao Zhao — 10 papers, h 6
  • Lingxiao Zhao — 4 papers, h 2
  • Lingxiao Zhao — 2 papers, h 1
  • Lingxiao Zhao — 2 papers, h 2
  • Lingxiao Zhao — 1 paper, 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
20192024
most citedFrom Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

27 citations · 103 across the 13 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.LG2020

On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights

Lingxiao Zhao, Leman Akoglu

It is common practice of the outlier mining community to repurpose classification datasets toward evaluating various detection models. To that end, often a binary classification da…

cs.LG2020

Connecting Graph Convolutional Networks and Graph-Regularized PCA

Lingxiao Zhao, Leman Akoglu

Graph convolution operator of the GCN model is originally motivated from a localized first-order approximation of spectral graph convolutions. This work stands on a different view;…

cs.LG2020

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

Jiong Zhu, Yujun Yan, Lingxiao Zhao +3

We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i.e., in networks where connect…

eess.SP2020

Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising

Siheng Chen, Yonina C. Eldar, Lingxiao Zhao

We propose an interpretable graph neural network framework to denoise single or multiple noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to th…

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