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Xiang Wang
Researcher at Central South University
Publications - 649
Citations - 37837
Xiang Wang is an academic researcher from Central South University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 81, co-authored 515 publications receiving 24199 citations. Previous affiliations of Xiang Wang include Northeast Normal University & University of Maryland, College Park.
Papers
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Journal ArticleDOI
Cytotoxicity of Carbon Nanomaterials: Single-Wall Nanotube, Multi-Wall Nanotube, and Fullerene
TL;DR: Carbon nanomaterials with different geometric structures exhibit quite different cytotoxicity and bioactivity in vitro, although they may not be accurately reflected in the comparative toxicity in vivo.
Proceedings ArticleDOI
Neural Graph Collaborative Filtering
TL;DR: Wang et al. as discussed by the authors proposed Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.
Posted Content
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
TL;DR: This work proposes a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering, and is much easier to implement and train, exhibiting substantial improvements over Neural Graph Collaborative Filtering (NGCF) under exactly the same experimental setting.
Proceedings ArticleDOI
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
TL;DR: LightGCN as mentioned in this paper learns user and item embedding by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding.
Proceedings ArticleDOI
Neural Graph Collaborative Filtering
TL;DR: This work develops a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.