Neural Multi-network Diffusion towards Social Recommendation

no code implementations • 11 Apr 2023 • Boxin Du, Lihui Liu, Jiejun Xu, Fei Wang, Hanghang Tong

Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation.

FairGen: Towards Fair Graph Generation

no code implementations • 30 Mar 2023 • Lecheng Zheng, Dawei Zhou, Hanghang Tong, Jiejun Xu, Yada Zhu, Jingrui He

In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability.

Data Augmentation Fairness +3

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification

1 code implementation • 5 Jun 2019 • Jun Wu, Jingrui He, Jiejun Xu

Graph data widely exist in many high-impact applications.

General Classification Graph Classification +3

Deep Adversarial Network Alignment

no code implementations • 27 Feb 2019 • Tyler Derr, Hamid Karimi, Xiaorui Liu, Jiejun Xu, Jiliang Tang

Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure.

Graph Embedding Network Embedding