Search Results for author: Jinhuan Wang

Found 6 papers, 1 papers with code

Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry

1 code implementation7 Jan 2024 Zeyu Wang, Tianyi Jiang, Jinhuan Wang, Qi Xuan

Molecular property prediction refers to the task of labeling molecules with some biochemical properties, playing a pivotal role in the drug discovery and design process.

Data Augmentation Drug Discovery +4

Subgraph Networks Based Contrastive Learning

no code implementations6 Jun 2023 Jinhuan Wang, Jiafei Shao, Zeyu Wang, Shanqing Yu, Qi Xuan, Xiaoniu Yang

In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information.

Attribute Contrastive Learning +3

Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning

no code implementations4 May 2023 Dayuan Chen, Jian Zhang, Yuqian Lv, Jinhuan Wang, Hongjie Ni, Shanqing Yu, Zhen Wang, Qi Xuan

Furthermore, most methods concentrate on a single attack goal and lack a generalizable adversary to develop distinct attack strategies for diverse goals, thus limiting precise control over victim model behavior in real-world scenarios.

Specificity

TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts

no code implementations18 Apr 2021 Jinhuan Wang, Pengtao Chen, Shanqing Yu, Qi Xuan

In this paper, we propose a Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts in Ethereum.

Graph Representation Learning

Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria

no code implementations4 Feb 2020 Qi Xuan, Yalu Shan, Jinhuan Wang, Zhongyuan Ruan, Guanrong Chen

It is found that both DALR and DILR are more effective than RLR, in the sense that rewiring a smaller number of links can succeed in the same attack.

Social and Information Networks Physics and Society

Subgraph Networks with Application to Structural Feature Space Expansion

no code implementations21 Mar 2019 Qi Xuan, Jinhuan Wang, Minghao Zhao, Junkun Yuan, Chenbo Fu, Zhongyuan Ruan, Guanrong Chen

In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.

General Classification Graph Classification +1

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