Qin Lv

9.8k citations
144 papers · 5.3k indexed · 3 hit papers · h-index 36

Impact in

Papers in

Qin Lv

141 papers receiving 5.2k citations

Hit Papers

Cross-modal Ambiguity Learning for Multimodal Fake News Detection 2022 · 166 citations
1662002202620102018100200300400500

Peers

Qin Lv
Comparison fields: 5 of 146
  • Computer Science Applications 372
  • Computer Networks and Communications 1.5k
  • Signal Processing 679
  • Transportation 366
  • Computer Vision and Pattern Recognition 1.0k
Replace Sasu Tarkoma with:
Sasu Tarkoma Finland
Michael Beigl Germany
Lina Yao Australia
John Krumm United States
Kevin I‐Kai Wang New Zealand
Huadóng Ma China
Rui Zhang China
Robert P. Dick United States
Liang Liu China
Bo Liu China
Qin Lv relative to Sasu Tarkoma Finland Sasu Tarkoma's profile →
Citations per field
00.5×1.5×
Sasu Tarkoma · 1×
Citations per year

Countries citing papers authored by Qin Lv

Since Specialization
Citations

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

Fields of papers citing papers by Qin Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Qin Lv, 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 Qin Lv Line = papers co-authored together Qin Lv links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20242
2 20245
3 20242
4 20241
5 20243
6 202323
7 20238
8 20236
9 202327
10 20220
11 202170
12 20198
13 2019110
14 201956
15 201830
16 2018130
17 201726
18
Low-rank matrix approximation with stability
201624
19
Analyzing Negative User Behavior in a Semi-anonymous Social Network
201410
20
Filtering Image Spam with Near-Duplicate Detection.
200774

About Qin Lv

Qin Lv is a scholar working on Computer Science Applications, Transportation, Communication, Computer Vision and Pattern Recognition and Computational Mathematics, having authored 144 papers that have together received 5.3k indexed citations. Recurring topics across this work include Indoor and Outdoor Localization Technologies (19 papers), Caching and Content Delivery (18 papers), Human Mobility and Location-Based Analysis (15 papers), Recommender Systems and Techniques (14 papers), Advanced Image and Video Retrieval Techniques (14 papers), Mobile Crowdsensing and Crowdsourcing (13 papers), Video Analysis and Summarization (11 papers) and Complex Network Analysis Techniques (11 papers). The work is most often cited by research in Computer Science Applications (372 citations), Computer Networks and Communications (1.5k citations), Signal Processing (679 citations), Transportation (366 citations) and Computer Vision and Pattern Recognition (1.0k citations). Qin Lv has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Li Shang, Pei Cao, Scott Shenker, Edith Cohen, Moses Charikar, Zhe Wang, William Josephson, Daqing Zhang, Kai Li and Shivakant Mishra. Their work appears in journals such as Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, IEEE Transactions on Mobile Computing, Knowledge-Based Systems, Proceedings of the ACM on Human-Computer Interaction and Atmospheric measurement techniques.

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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