Chuan Zhou

FraudNE: a Joint Embedding Approach for Fraud Detection

2018

Detecting fraudsters is a meaningful problem for both users and e-commerce platform. Existing graph-based approaches mainly adopt shallow models, which cannot capture the highly non-linear relationship between vertexes in a bipartite graph composed of users …

Unsupervised Domain Adaptive Graph Convolutional Networks

2020

Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the …

Deep Semantic Network Representation

2020

Network representation aims to learn low-dimensional vector representations of network nodes while preserving the inherent properties of the network. For all its popularity, majority of the existing methods focus on exploitation of diverse information, including …

Projective Ranking-based GNN Evasion Attacks

2022 · IEEE Transactions on Knowledge and Data Engineering

Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. However, GNNs are at risk of adversarial attacks. Two primary limitations of the current evasion attack methods are highlighted: (1) The currentGradArgmaxignores the “long-term” …

Design of Intelligent Grading System for College English Translation

2022 · Proceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022)

At present, the currently used intelligent scoring system for college English translation has the following defects in practical application: the running time is too long, and the final scoring result deviates greatly from the actual …

Social Recommendation with an Essential Preference Space

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Social recommendation, which aims to exploit social information to improve the quality of a recommender system, has attracted an increasing amount of attention in recent years. A large portion of existing social recommendation models are …