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researcher

Meng Liu

National University of Defense Technology

10 papers hereh-index 151.1k citations22 works total

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

author position
  • first author4
  • middle author5

Across the 9 of 10 papers where every author was matched, so the position is known.

fields
  • cs.AI4
  • cs.LG4
  • cs.CR1
  • cs.SD1
affiliations
  • National University of Defense Technology
same name
  • Meng Liu — 66 papers, h 6
  • Meng Liu — 15 papers, h 16
  • Meng Liu — 15 papers, h 6
  • Meng Liu — 14 papers, h 6
  • Meng Liu — 7 papers, h 3
  • Meng Liu — 6 papers, h 12

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

most citedSelf-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment

100 citations · 235 across the 10 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023★ 2 cited

Reinforcement Graph Clustering with Unknown Cluster Number

Yue Liu, Ke Liang, Jun Xia +5

Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks in an unsupervised manner, has attracted great attention in recent years. Although the pe…

cs.LG2023★ 25 cited

Deep Temporal Graph Clustering

Meng Liu, Yue Liu, Ke Liang +4

Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevert…

cs.LG2023★ 2 cited

SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning

Lingyuan Meng, Ke Liang, Bin Xiao +5

Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer long-tail data-poor relations, which has drawn increasing attention these years due to its practicalities. Th…

cs.LG2023★ 100 cited

Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment

Meng Liu, Ke Liang, Yawei Zhao +5

Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.