Heterogeneous Graph Node Classification with Multi-Hops Relation Features
* External authors
In recent years, knowledge graph~(KG) has obtained many achievements in both research and industrial fields. However, most KG algorithms consider node embedding with only structure and node features, but not relation features. In this paper, we propose a novel Heterogeneous Attention~(HAT) algorithm and use both node-based and path-based attention mechanisms to learn various types of nodes and edges on the KG. To better capture representations, multi-hop relation features are involved to generate edge embeddings and help the model obtain more semantic information. To capture a more complex representation, we design different encoder parameters for different types of nodes and edges in HAT. Extensive experiments validate that our HAT significantly outperforms the state-of-the-art methods on both the public datasets and a large-scale real-world fintech dataset.
Privacy for Free: How does Dataset Condensation Help Privacy?
To prevent unintentional data leakage, research community has resorted to data generators that can produce differentially private data for model training. However, for the sake of the data privacy, existing solutions suffer from either expensive training cost or poor general…
Accelerated Federated Learning with Decoupled Adaptive Optimization
The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients. Recently, many heuristics efforts have been made to generalize centralized adaptive optimization methods, such as S…
A Federated Graph Neural Network Framework for Privacy-Preserving Personalization
Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have conside…