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researcher

Mihai Cucuringu

12 papers hereh-index 8269 citations16 works total

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

author position
  • first author8
  • middle author3
  • last author1

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • stat.ML8
  • cs.LG2
  • cs.SI1
  • math.ST1
same name
  • Mihai Cucuringu — 30 papers, h 12
  • Mihai Cucuringu — 20 papers, h 11
  • Mihai Cucuringu — 11 papers, h 10
  • Mihai Cucuringu — 5 papers, h 3
  • Mihai Cucuringu — 5 papers, h 2
  • Mihai Cucuringu — 3 papers, h 2

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
20162024
most citedSPONGE: A generalized eigenproblem for clustering signed networks

26 citations · 82 across the 12 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019★ 13 cited

Hermitian matrices for clustering directed graphs: insights and applications

Mihai Cucuringu, Huan Li, He Sun +1

Graph clustering is a basic technique in machine learning, and has widespread applications in different domains. While spectral techniques have been successfully applied for cluste…

stat.ML2019★ 17 cited

Ranking and synchronization from pairwise measurements via SVD

Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi

Given a measurement graph G=(V,E) and an unknown signal r∈Rn, we investigate algorithms for recovering r from pairwise measurements of the form ri​−rj​; $\…

stat.ML2019★ 26 cited

SPONGE: A generalized eigenproblem for clustering signed networks

Mihai Cucuringu, Peter Davies, Aldo Glielmo +1

We introduce a principled and theoretically sound spectral method for k-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negativ…

cs.SI2019

An MBO scheme for clustering and semi-supervised clustering of signed networks

Mihai Cucuringu, Andrea Pizzoferrato, Yves van Gennip

We introduce a principled method for the signed clustering problem, where the goal is to partition a graph whose edge weights take both positive and negative values, such that edge…

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