Lan Nie

1.1k total citations · 1 hit paper
12 papers, 702 citations indexed

About

Lan Nie is a scholar working on Information Systems, Artificial Intelligence and Statistical and Nonlinear Physics. According to data from OpenAlex, Lan Nie has authored 12 papers receiving a total of 702 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Information Systems, 7 papers in Artificial Intelligence and 4 papers in Statistical and Nonlinear Physics. Recurrent topics in Lan Nie's work include Spam and Phishing Detection (4 papers), Complex Network Analysis Techniques (4 papers) and Web Data Mining and Analysis (4 papers). Lan Nie is often cited by papers focused on Spam and Phishing Detection (4 papers), Complex Network Analysis Techniques (4 papers) and Web Data Mining and Analysis (4 papers). Lan Nie collaborates with scholars based in United States. Lan Nie's co-authors include Brian D. Davison, Xiaoguang Qi, Dan Liu, H. Brendan McMahan, Eugene Davydov, Sharat Chikkerur, Jeremy Kubica, Gary D. Holt, Michael Young and Martin Wattenberg and has published in prestigious journals such as Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, International Conference on Computational Linguistics and National Conference on Artificial Intelligence.

In The Last Decade

Lan Nie

12 papers receiving 632 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Ad click prediction

2013 509 citations H. Brendan McMahan, Gary D. Holt et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Lan Nie United States	8	379	361	171	132	95	12	702
Haibin Cheng United States	13	252 0.7×	375 1.0×	153 0.9×	82 0.6×	129 1.4×	17	655
Pipei Huang China	7	654 1.7×	549 1.5×	299 1.7×	120 0.9×	125 1.3×	10	952
Yu-Chin Juan Taiwan	6	469 1.2×	337 0.9×	296 1.7×	84 0.6×	84 0.9×	7	676
Cihan Kaleli Türkiye	12	666 1.8×	457 1.3×	178 1.0×	103 0.8×	123 1.3×	29	854
Wei-Sheng Chin Taiwan	5	431 1.1×	321 0.9×	266 1.6×	78 0.6×	79 0.8×	6	625
Junfeng Pan China	4	253 0.7×	233 0.6×	159 0.9×	92 0.7×	69 0.7×	4	534
Mohamed Aly United States	13	345 0.9×	659 1.8×	269 1.6×	56 0.4×	185 1.9×	28	1.1k
Weiwen Liu China	16	367 1.0×	448 1.2×	164 1.0×	94 0.7×	58 0.6×	54	750
Jinoh Oh South Korea	14	812 2.1×	662 1.8×	324 1.9×	133 1.0×	153 1.6×	30	1.1k

Countries citing papers authored by Lan Nie

Since Specialization

Citations

This map shows the geographic impact of Lan Nie'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 Lan Nie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lan Nie more than expected).

Fields of papers citing papers by Lan Nie

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lan Nie. 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 Lan Nie. The network helps show where Lan Nie may publish in the future.

Co-authorship network of co-authors of Lan Nie

This figure shows the co-authorship network connecting the top 25 collaborators of Lan Nie. A scholar is included among the top collaborators of Lan Nie based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lan Nie. Lan Nie is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

1.

Ma, Nan, et al.. (2022). An Online Multi-task Learning Framework for Google Feed Ads Auction Models. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3477–3485. 8 indexed citations

2.

McMahan, H. Brendan, Gary D. Holt, D. Sculley, et al.. (2013). Ad click prediction. 1222–1230. 509 indexed citations breakdown →

3.

Nie, Lan, et al.. (2010). Resolving Surface Forms to Wikipedia Topics. International Conference on Computational Linguistics. 1335–1343. 43 indexed citations

4.

Nie, Lan, et al.. (2010). Learning Document Labels from Enriched Click Graphs. 57–64. 1 indexed citations

5.

Nie, Lan & Brian D. Davison. (2008). Separate and inequal. 443–450. 6 indexed citations

6.

Nie, Lan, Brian D. Davison, & Baoning Wu. (2007). From whence does your authority come?: utilizing community relevance in ranking. National Conference on Artificial Intelligence. 1421–1426. 11 indexed citations

7.

Nie, Lan, Baoning Wu, & Brian D. Davison. (2007). Winnowing wheat from the chaff. 869–870. 13 indexed citations

8.

Nie, Lan, Brian D. Davison, & Baoning Wu. (2007). Ranking by community relevance. 873–874. 3 indexed citations

9.

Qi, Xiaoguang, Lan Nie, & Brian D. Davison. (2007). Measuring similarity to detect qualified links. 49–56. 13 indexed citations

10.

Nie, Lan, Baoning Wu, & Brian D. Davison. (2007). A cautious surfer for PageRank. 1119–1120. 8 indexed citations

11.

Nie, Lan, Brian D. Davison, & Xiaoguang Qi. (2006). Topical link analysis for web search. 91–98. 85 indexed citations

12.

Mao, Song, Lan Nie, & Grid Thoma. (2005). Unsupervised style classification of document page images. 208. II–510. 2 indexed citations

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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