Ju Fan

3.2k citations
93 papers · 2.2k · 1 hit paper · h-index 24

Impact in

Papers in

Ju Fan

88 papers receiving 2.2k citations

Ju Fan's Hit Papers

Influence Maximization on Social Graphs: A Survey 2018 · 453 citations
4530+2+5Years since publication100200300400

Peers

Ju Fan
Comparison fields: 5 of 111
  • Computer Science Applications 428
  • Statistical and Nonlinear Physics 660
  • Management Science and Operations Research 443
  • Artificial Intelligence 1.0k
  • Signal Processing 290
Replace Adam Marcus with:
Adam Marcus United States
Aris Anagnostopoulos Italy
Theodoros Lappas United States
Mausam Mausam India
Sreenivas Gollapudi United States
Pavel Serdyukov Russia
Cheng–Te Li Taiwan
Pasquale De Meo Italy
Yanyan Lan China
Panagiotis Symeonidis Greece
Ju Fan relative to Adam Marcus United States Adam Marcus's profile →
Citations per field
00.5×7.7×
Adam Marcus · 1×
Citations per year

Countries citing papers authored by Ju Fan

Since Specialization
Citations

This map shows the geographic impact of Ju Fan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ju Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ju Fan more than expected).

Fields of papers citing papers by Ju Fan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ju Fan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ju Fan. The network helps show where Ju Fan may publish in the future.

Co-authors

The 25 scholars most cited alongside Ju Fan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ju Fan Line = papers co-authored together Ju Fan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 93 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Influence Maximization on Social Graphs: A Survey
Hit paper breakdown →
2018453
2 2015185
3 2015139
4 202088
5 201481
6 201770
7 201758
8 201255
9 201852
10 202144
11 202142
12 201540
13 201139
14 201737
15 202036
16 202435
17 202035
18 202333
19 201832
20 201430

About Ju Fan

Ju Fan is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Computer Science Applications and Signal Processing, having authored 93 papers that have together received 2.2k indexed citations. Recurring topics across this work include Data Quality and Management (22 papers), Mobile Crowdsensing and Crowdsourcing (22 papers), Topic Modeling (17 papers), Privacy-Preserving Technologies in Data (17 papers), Data Stream Mining Techniques (16 papers), Web Data Mining and Analysis (13 papers), Data Management and Algorithms (12 papers) and Complex Network Analysis Techniques (10 papers). The work is most often cited by research in Computer Science Applications (428 citations), Statistical and Nonlinear Physics (660 citations), Management Science and Operations Research (443 citations), Artificial Intelligence (1.0k citations) and Signal Processing (290 citations). Ju Fan has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Kian‐Lee Tan, Guoliang Li, Yuchen Li, Yanhao Wang, Jianhua Feng, Beng Chin Ooi, Xiaoyong Du, Lizhu Zhou, Meihui Zhang and Nan Tang. Their work appears in journals such as Proceedings of the VLDB Endowment, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, The VLDB Journal and Journal of Medical Internet Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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