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researcher

X. Shen

21 papers hereh-index 171.3k citations82 works total

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

author position
  • sole author1
  • first author6
  • middle author5
  • last author8

Across the 20 of 21 papers where every author was matched, so the position is known.

fields
  • math.PR8
  • cs.SI5
  • cs.LG4
  • cond-mat.mtrl-sci2
  • physics.comp-ph1
  • stat.ME1
same name
  • X. Shen — 293 papers, h 50
  • X. Shen — 254 papers, h 20
  • X. Shen — 122 papers, h 6
  • X. Shen — 114 papers
  • X. Shen — 76 papers, h 12
  • X. Shen — 34 papers, h 78

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
20192026
most citedDeep Network Embedding for Graph Representation Learning in Signed Networks

114 citations · 286 across the 20 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

math.PR2019★ 3 cited

Coalescence estimates for the corner growth model with exponential weights

Timo Seppäläinen, Xiao Shen

We establish estimates for the coalescence time of semi-infinite directed geodesics in the planar corner growth model with i.i.d. exponential weights. There are four estimates: upp…

cs.LG2019

Adversarial Training Methods for Network Embedding

Quanyu Dai, Xiao Shen, Liang Zhang +2

Network Embedding is the task of learning continuous node representations for networks, which has been shown effective in a variety of tasks such as link prediction and node classi…

cs.SI2019★ 114 cited

Deep Network Embedding for Graph Representation Learning in Signed Networks

Xiao Shen, Fu-Lai Chung

Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dime…

cs.SI2019★ 107 cited

Network Together: Node Classification via Cross network Deep Network Embedding

Xiao Shen, Quanyu Dai, Sitong Mao +2

Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing network…

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