Hierarchical graph transformer with adaptive node sampling
The Transformer architecture has achieved remarkable success in a number of domains
including natural language processing and computer vision. However, when it comes to …
including natural language processing and computer vision. However, when it comes to …
Spectral feature augmentation for graph contrastive learning and beyond
Although augmentations (eg, perturbation of graph edges, image crops) boost the efficiency
of Contrastive Learning (CL), feature level augmentation is another plausible …
of Contrastive Learning (CL), feature level augmentation is another plausible …
A survey of trustworthy federated learning: Issues, solutions, and challenges
Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
Optimal block-wise asymmetric graph construction for graph-based semi-supervised learning
Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the
underlying manifold structures of samples in high-dimensional spaces. It involves two …
underlying manifold structures of samples in high-dimensional spaces. It involves two …
Contrastive cross-scale graph knowledge synergy
Graph representation learning via Contrastive Learning (GCL) has drawn considerable
attention recently. Efforts are mainly focused on gathering more global information via …
attention recently. Efforts are mainly focused on gathering more global information via …
Opengsl: A comprehensive benchmark for graph structure learning
Abstract Graph Neural Networks (GNNs) have emerged as the de facto standard for
representation learning on graphs, owing to their ability to effectively integrate graph …
representation learning on graphs, owing to their ability to effectively integrate graph …
Bipartite graph convolutional hashing for effective and efficient top-n search in hamming space
Searching on bipartite graphs is basal and versatile to many real-world Web applications,
eg, online recommendation, database retrieval, and query-document searching. Given a …
eg, online recommendation, database retrieval, and query-document searching. Given a …
A survey of trustworthy federated learning with perspectives on security, robustness and privacy
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly
benefited human society. Among various AI technologies, Federated Learning (FL) stands …
benefited human society. Among various AI technologies, Federated Learning (FL) stands …
No change, no gain: empowering graph neural networks with expected model change maximization for active learning
Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …
graph-structured data, but their success depends on sufficient labeled data. We present a …
WSFE: wasserstein sub-graph feature encoder for effective user segmentation in collaborative filtering
Maximizing the user-item engagement based on vectorized embeddings is a standard
procedure of recent recommender models. Despite the superior performance for item …
procedure of recent recommender models. Despite the superior performance for item …