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A. Zeng

40 papers hereh-index 282.9k citations96 works total

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

author position
  • sole author1
  • first author15
  • middle author17
  • last author6

Across the 39 of 40 papers where every author was matched, so the position is known.

fields
  • physics.soc-ph28
  • cs.IR7
  • cs.SI4
  • cond-mat.dis-nn1
same name
  • A. Zeng — 39 papers, h 34
  • A. Zeng — 1 paper, h 11
  • A. Zeng — 1 paper, h 3
  • A. Zeng — 1 paper, h 11

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
20092021
most citedRanking spreaders by decomposing complex networks

449 citations · 1.6k across the 40 of their papers we have counts for

collaborators
Showing cs.SIShow all

4 papers · 1 filter

cs.SI2020

Limits of PageRank-based ranking methods in sports data

Yuhao Zhou, Ruijie Wang, Yi-Cheng Zhang +2

While PageRank has been extensively used to rank sport tournament participants (teams or individuals), its superiority over simpler ranking methods has been never clearly demonstra…

cs.SI2014★ 18 cited

Temporal effects in trend prediction: identifying the most popular nodes in the future

Yanbo Zhou, An Zeng, Wei-Hong Wang

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the fut…

cs.SI2013★ 61 cited

Ranking users, papers and authors in online scientific communities

Hao Liao, Rui Xiao, Giulio Cimini +1

The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growi…

cs.SI2013★ 34 cited

Trend prediction in temporal bipartite networks: the case of Movielens, Netflix, and Digg

An Zeng, Stanislao Gualdi, Matus Medo +1

Online systems where users purchase or collect items of some kind can be effectively represented by temporal bipartite networks where both nodes and links are added with time. We u…

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