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Juncheng Liu

9 papers hereh-index 10491 citations15 works total

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

author position
  • first author4
  • middle author4
  • last author1

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

fields
  • cs.CL3
  • cs.LG3
  • cs.SI2
  • cs.CR1
same name
  • Juncheng Liu — 8 papers, h 6
  • Juncheng Liu — 5 papers, h 2
  • Juncheng Liu — 4 papers, h 3
  • Juncheng Liu — 2 papers, h 4
  • Juncheng Liu — 2 papers, h 2
  • Juncheng Liu — 1 paper, h 5

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
20202025
most citedPANE: scalable and effective attributed network embedding

24 citations · 45 across the 9 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.LG2022★ 6 cited

MGNNI: Multiscale Graph Neural Networks with Implicit Layers

Juncheng Liu, Bryan Hooi, Kenji Kawaguchi +1

Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. In this paper, we introduce and justify two weaknesses o…

cs.CL2022★ 1 cited

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis

Yiwei Wang, Muhao Chen, Wenxuan Zhou +6

Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations. As…

cs.CL2022

Dangling-Aware Entity Alignment with Mixed High-Order Proximities

Juncheng Liu, Zequn Sun, Bryan Hooi +5

We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of…

cs.LG2022★ 8 cited

EIGNN: Efficient Infinite-Depth Graph Neural Networks

Juncheng Liu, Kenji Kawaguchi, Bryan Hooi +2

Graph neural networks (GNNs) are widely used for modelling graph-structured data in numerous applications. However, with their inherently finite aggregation layers, existing GNN mo…

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