Deep Graph Representation Learning and Optimization for Influence Maximization

1 code implementation • 1 May 2023 • Chen Ling, Junji Jiang, Junxiang Wang, My Thai, Lukas Xue, James Song, Meikang Qiu, Liang Zhao

Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users.

Graph Representation Learning

Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks

1 code implementation • 10 Nov 2022 • Khang Tran, Phung Lai, NhatHai Phan, Issa Khalil, Yao Ma, Abdallah Khreishah, My Thai, Xintao Wu

Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph data.

Bit-aware Randomized Response for Local Differential Privacy in Federated Learning

no code implementations • 29 Sep 2021 • Phung Lai, Hai Phan, Li Xiong, Khang Phuc Tran, My Thai, Tong Sun, Franck Dernoncourt, Jiuxiang Gu, Nikolaos Barmpalios, Rajiv Jain

In this paper, we develop BitRand, a bit-aware randomized response algorithm, to preserve local differential privacy (LDP) in federated learning (FL).

Federated Learning Image Classification