Xin Chen

Postdoctoral Researcher

Key Laboratory of Data Engineering and Knowledge Engineering

About Me

Dr. Xin Chen is a Postdoctoral Researcher at the Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University of China. His research focuses on developing efficient query and computational techniques to advance the visual analytics of large-scale datasets.

Interests

Information Visualization
Human-Computer Interaction
Big Data Analytics

Education

Ph.D. Computer Science and Technology
Shandong University
B.Eng. Software Engineering
Shandong University

Featured Publications

Graph Layout

SNAP-tFDP: Massively Scalable Graph Layouts via Sparse Negative Sampling

Results Comparison of classical spring-electrical models and t-FDP models with different weighting schemes. Comparison of different degree-weighting schemes for (a) two high-degree nodes and (b) a high-degree node and a low-degree node. The attractive force Fat-FDP is identical in all cases and is shown as a black solid line. Product-based weighting (orange) produces overly strong relative repulsion and its normalized variant (purple) nearly eliminates repulsion for low-degree nodes, while linearly normalized degree weighting (red) provides effective balance.

08-05-2026

Query Optimization

Visualization-Oriented Progressive Time Series Transformation

Results Visual analysis of multiple time series from a dataset of NYSE-listed stocks. Analysts may casually apply and compose various point-wise transformations to explore interesting patterns, expecting timely and highly accurate visualizations.

08-24-2025

Density Plot

Visualization-Driven Illumination for Density Plots

Results Parameter analysis on the Hertzsprung-Russell diagram dataset [31]. (a,b,c,d) Increasing η makes low-density structures more salient while the high-density structures remain unchanged. (e,f,c,g,h) Decreasing φ makes low-density structures clearer, but at the same time darkens high-density structures.

02-01-2025

Scatterplots

Pyramid-based Scatterplots Sampling for Progressive and Streaming Data Visualization

Results Parameter analysis on the Person Activity dataset [16] with the associated PDDr and ESRr scores (see Section 5). The orange dashed boxes and red dashed circles represent typical low- and medium-density regions, respectively. (a,b,c) A large λ results in more regions classified as low-density regions; (d,b,e) a large ω results in more display samples in low-density regions; (f) when λ and ω are both large, almost all low-density regions in the original scatterplot are kept but the data density ratios cannot be maintained; (b,g,h) decreasing stopLevel introduces more display samples; outliers in low-density regions can be shown more clearly but overplotting will have resulted.

01-01-2022

Recent Publications

Xin Chen, Shuowei Hou, Yifan Wang, Mingliang Xue, Zezheng Feng, Oliver Deussen, Weidong Huang, Yunhai Wang (2026). SNAP-tFDP: Massively Scalable Graph Layouts via Sparse Negative Sampling. IEEE VIS.

PDF Cite Code Dataset Project

Xin Chen, Lingyu Zhang, Huaiwei Bao, Wei Lu, Eugene Wu, Xiaohui Yu, Yunhai Wang (2025). Visualization-Oriented Progressive Time Series Transformation. In ACM SIGMOD.

PDF Cite Code Project Video DOI

Huaiwei Bao, Xin Chen, Kecheng Lu, Chi-Wing Fu, Jean-Daniel Fekete, Yunhai Wang (2025). Bi-Scale Density-Plot Enhancement Based on Variance-Aware Filter. CAG.

PDF Cite Code DOI

Xin Chen, Yunhai Wang, Huaiwei Bao, Kecheng Lu, Jaemin Jo, Chi-Wing Fu, Jean-Daniel Fekete (2025). Visualization-Driven Illumination for Density Plots. IEEE TVCG.

PDF Cite Code Project DOI

Yunhai Wang, Yuchun Wang, Xin Chen, Yue Zhao, Fan Zhang, Eugene Wu, Chi-Wing Fu, Xiaohui Yu (2023). OM3: An Ordered Multi-level Min-Max Representation for Interactive Progressive Visualization of Time Series. In ACM SIGMOD.

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Professional Services

Committee Member, CSIG-VIS
Short Papers Program Committee, Vis 2025-2026
Reviewer, EuroVis 2025, ChinaVis 2026