1 code implementation • 18 Apr 2023 • Sina Sajadmanesh, Daniel Gatica-Perez
Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information.
1 code implementation • 2 Mar 2022 • Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet, Daniel Gatica-Perez
In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP).
1 code implementation • 9 Jun 2020 • Sina Sajadmanesh, Daniel Gatica-Perez
In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private, but they could be beneficial for a central server for training a GNN over the graph.
1 code implementation • 30 Sep 2017 • Sina Sajadmanesh, Sogol Bazargani, Jiawei Zhang, Hamid R. Rabiee
In this paper, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks.
no code implementations • 21 Jun 2017 • Sina Sajadmanesh, Jiawei Zhang, Hamid R. Rabiee
In this paper, we try to solve the problem of temporal link prediction in information networks.
1 code implementation • 8 Mar 2017 • Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi
To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result.
no code implementations • 26 Oct 2016 • Sina Sajadmanesh, Sina Jafarzadeh, Seyed Ali Osia, Hamid R. Rabiee, Hamed Haddadi, Yelena Mejova, Mirco Musolesi, Emiliano De Cristofaro, Gianluca Stringhini
In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world.
1 code implementation • IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2016 • Sina Sajadmanesh, Hamid R. Rabiee, Ali Khodadadi
Once a user (called source user) of a social network (called source network) joins a new social network (called target network), a new inter-network link (called anchor link) is formed between the source and target networks.
Social and Information Networks Physics and Society