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Kai Zhou

The Hong Kong Polytechnic University

11 papers hereh-index 10378 citations35 works total

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

author position
  • first author3
  • middle author4
  • last author4

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.SI4
  • cs.CR2
  • cs.AI1
affiliations
  • The Hong Kong Polytechnic University
Homepage
same name
  • Kai Zhou — 11 papers, h 10
  • Kai Zhou — 9 papers, h 4
  • Kai Zhou — 8 papers, h 5
  • Kai Zhou — 8 papers, h 2
  • Kai Zhou — 7 papers, h 2
  • Kai Zhou — 7 papers, h 4

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
20182026
most citedRobust Collective Classification against Structural Attacks

2 citations · 2 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2024

Adversarial Robustness of Link Sign Prediction in Signed Graphs

Jialong Zhou, Xing Ai, Yuni Lai +7

Signed graphs serve as fundamental data structures for representing positive and negative relationships in social networks, with signed graph neural networks (SGNNs) emerging as th…

cs.LG2023

Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach

Xing Ai, Jialong Zhou, Yulin Zhu +4

Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. Howev…

cs.LG2023

Robust Graph Contrastive Learning with Information Restoration

Yulin Zhu, Xing Ai, Yevgeniy Vorobeychik +1

The graph contrastive learning (GCL) framework has gained remarkable achievements in graph representation learning. However, similar to graph neural networks (GNNs), GCL models are…

cs.LG2020★ 2 cited

Robust Collective Classification against Structural Attacks

Kai Zhou, Yevgeniy Vorobeychik

Collective learning methods exploit relations among data points to enhance classification performance. However, such relations, represented as edges in the underlying graphical mod…

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