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Hao Peng

4 papers hereh-index 5109 citations13 works total

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

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.AI1
same name
  • Hao Peng — 21 papers, h 37
  • Hao Peng — 20 papers, h 10
  • Hao Peng — 19 papers
  • Hao Peng — 14 papers, h 20
  • Hao Peng — 13 papers
  • Hao Peng — 11 papers, h 8

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

most citedDeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2026

Trust-But-Verify: Poisoning-Resilient Locally Private Graph Learning Protocols

Longzhu He, Li Sun, Hao Peng +3

Built upon local differential privacy (LDP), locally private graph learning protocols have emerged as an important paradigm for decentralized graph learning, balancing privacy prot…

cs.AI2026

FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation

Yingguang Yang, Hao Liu, Xin Zhang +8

Social bot detection is critical to the stability and security of online social platforms. However, current state-of-the-art bot detection models are largely developed in isolation…

cs.LG2024★ 1 cited

DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

Li Sun, Zhenhao Huang, Hua Wu +4

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined gr…

cs.LG2024

Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning

Li Sun, Zhenhao Huang, Zixi Wang +3

Graphs are typical non-Euclidean data of complex structures. In recent years, Riemannian graph representation learning has emerged as an exciting alternative to Euclidean ones. How…

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