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Liang Wang

4 papers hereh-index 10581 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.AI1
  • cs.CL1
same name
  • Liang Wang — 55 papers
  • Liang Wang — 48 papers
  • Liang Wang — 15 papers, h 94
  • Liang Wang — 13 papers
  • Liang Wang — 10 papers
  • Liang Wang — 8 papers, h 27

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192022
most citedMeta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily

22 citations · 45 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CL2022★ 2 cited

Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks

Junfei Wu, Weizhi Xu, Qiang Liu +2

The prevalence and perniciousness of fake news have been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper,…

cs.LG2022★ 22 cited

Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily

Xiaojun Ma, Qin Chen, Yuanyi Ren +2

Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream…

cs.LG2020

Streaming Graph Neural Networks via Continual Learning

Junshan Wang, Guojie Song, Yi Wu +1

Graph neural networks (GNNs) have achieved strong performance in various applications. In the real world, network data is usually formed in a streaming fashion. The distributions o…

cs.AI2019★ 21 cited

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Wen Zhang, Bibek Paudel, Liang Wang +5

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings c…

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