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researcher

Jiajun Zhou

Zhejiang University of Technology

39 papers hereh-index 12535 citations62 works total

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

author position
  • first author12
  • middle author22
  • last author1

Across the 35 of 39 papers where every author was matched, so the position is known.

fields
  • cs.LG14
  • cs.CR12
  • cs.SI8
  • cs.AI1
  • cs.CL1
  • cs.SE1
affiliations
  • Zhejiang University of Technology
same name
  • Jiajun Zhou — 7 papers, h 4
  • Jiajun Zhou — 3 papers, h 4
  • Jiajun Zhou — 2 papers
  • Jiajun Zhou — 2 papers, h 2

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
20192026
most citedBehavior-aware Account De-anonymization on Ethereum Interaction Graph

109 citations · 339 across the 38 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.SI2023★ 1 cited

Multi-triplet Feature Augmentation for Ponzi Scheme Detection in Ethereum

Chengxiang Jin, Jiajun Zhou, Shengbo Gong +2

Blockchain technology revolutionizes the Internet, but also poses increasing risks, particularly in cryptocurrency finance. On the Ethereum platform, Ponzi schemes, phishing scams,…

cs.LG2023★ 1 cited

PathMLP: Smooth Path Towards High-order Homophily

Jiajun Zhou, Chenxuan Xie, Shengbo Gong +4

Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph n…

cs.LG2023★ 6 cited

Clarify Confused Nodes via Separated Learning

Jiajun Zhou, Shengbo Gong, Xuanze Chen +4

Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented tasks. However, real-world graphs invariably contain a certain proportion of heterophilous nodes, c…

cs.LG2023★ 13 cited

Neighborhood Homophily-based Graph Convolutional Network

Shengbo Gong, Jiajun Zhou, Chenxuan Xie +1

Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks. However, many real-world graphs are heterophilous, challenging the homophily assumption of classical…

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