Jiacheng Pan

English | ็ฎ€ไฝ“ไธญๆ–‡

๐Ÿ‘ป Personal Information

Jiacheng Pan is now a Ph.D. student in State Key Lab of CAD&CG at Zhejiang University, China. His current research interests include visualization, visual analytics. From 2013 to 2017, he was an undergraduate student in Zhejiang University. Thereafter he received his Bachelorโ€™s degree and became a Master student in State Key Lab of CAD&CG at Zhejiang University. In September, 2019, he became a Ph.D. student under Professor Wei Chen.

๐Ÿ“š Education

2017 ~ Present: Master-Ph.D.

Zhejiang University

Major: Visualization and Visual Analytics

2013 ~ 2017: Bachelor

Zhejiang University

Major: Software Engineering

Minor: Intensive Training Program of Innovation and Entrepreneurship

๐Ÿ“œ Publications

Jiacheng Pan has published seven papers, two of which are published as the first author. These papers mainly focus on graph visual exploration, graph embeddings, interactions of graph layout, graph visualization authoring and anomaly analytics of graph data.

2021

A Visual Analytics Approach for Structural Differences Among Graphs via Deep Learning

Representing and analyzing structural differences among graphs help gain insight into the difference related patterns such as dynamic evolutions of graphs. Conventional solutions leverage representation learning techniques to encode structural information, but lack an intuitive way of studying structural semantics of graphs. In this article, we propose a representation-and-analysis scheme for structural differences among graphs. We propose a deep-learning-based embedding technique to encode multiple graphs while preserving semantics of structural differences. We design and implement a web-based visual analytics system to support comparative study of features learned from the embeddings. One distinctive feature of our approach is that it supports semantics-aware construction, quantification, and investigation of latent relations encoded in graphs. We validate the usability and effectiveness of our approach through case studies with three datasets.

@article{HanPXZC20,
title = {A Visual Analytics Approach for Structural Differences among Transportation Networks},
journal = {IFAC-PapersOnLine},
volume = {53},
number = {5},
pages = {566-571},
year = {2020},
note = {3rd IFAC Workshop on Cyber-Physical & Human Systems CPHS 2020},
issn = {2405-8963},
doi = {https://doi.org/10.1016/j.ifacol.2021.04.226},
url = {https://www.sciencedirect.com/science/article/pii/S2405896321004110},
author = {Dongming Han and Jiacheng Pan and Cong Xie and Xiaodong Zhao and Wei Chen},
}

NetV.js: A web-based library for high-efficiency visualization of large-scale graphs and networks

Graph visualization plays an important role in several fields, such as social media networks, proteinโ€“protein interaction networks, and traffic networks. A number of visualization design tools and programming toolkits have been widely used in graph-related applications. However, a key challenge remains in the high-efficiency visualization of large-scale graph data. In this study, we present NetV.js, an open-source and WebGL-based JavaScript library that supports the fast visualization of large-scale graph data (up to 50 thousand nodes and 1 million edges) at an interactive frame rate with a commodity computer.

@article{HanPZC21,
title = {NetV.js: A web-based library for high-efficiency visualization of large-scale graphs and networks},
journal = {Visual Informatics},
volume = {5},
number = {1},
pages = {61-66},
year = {2021},
issn = {2468-502X},
doi = {https://doi.org/10.1016/j.visinf.2021.01.002},
url = {https://www.sciencedirect.com/science/article/pii/S2468502X21000048},
author = {Dongming Han and Jiacheng Pan and Xiaodong Zhao and Wei Chen},
keywords = {Graph, Graph visualization, Network visualization, Node-link diagram},
}

2020

We design and evaluate a novel layout fine-tuning technique for node-link diagrams that facilitates exemplar-based adjustment of a group of substructures in batching mode. The key idea is to transfer user modifications on a local substructure to other substructures in the entire graph that are topologically similar to the exemplar. We first precompute a canonical representation for each substructure with node embedding techniques and then use it for on-the-fly substructure retrieval. We design and develop a light-weight interactive system to enable intuitive adjustment, modification transfer, and visual graph exploration. We also report some results of quantitative comparisons, three case studies, and a within-participant user study.

@ARTICLE{PanCZZZZCFW21,
author={Pan, Jiacheng and Chen, Wei and Zhao, Xiaodong and Zhou, Shuyue and Zeng, Wei and Zhu, Minfeng and Chen, Jian and Fu, Siwei and Wu, Yingcai},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Exemplar-based Layout Fine-tuning for Node-link Diagrams},
year={2021},
volume={27},
number={2},
pages={1655-1665},
doi={10.1109/TVCG.2020.3030393}
}

iNet: Visual Analysis of Irregular Transition in Multivariate Dynamic Networks

Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time. They are common in multimedia applications. Anomaly detection is one of the essential tasks in analyzing these networks though it is not well addressed. In this paper, we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic networks. We conclude features of rare categories and two types of anomalies of rare categories. Then we present a novel rare category detection method, called DIRAD, to detect rare category candidates with anomalies. We develop a prototype system called iNet, which integrates two major visualization components, including a glyph-based rare category identifier, which helps users to identify rare categories among detected substructures, a major view, which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex attributes. Evaluations, including an algorithm performance evaluation, a case study, and a user study, are conducted to test the effectiveness of proposed methods.

@article{HanPPZCHXC22,
author = {Dongming Han and
Jiacheng Pan and
Rusheng Pan and
Dawei Zhou and
Nan Cao and
Jingrui He and
Mingliang Xu and
Wei Chen},
title = {iNet: visual analysis of irregular transition in multivariate dynamic networks},
journal = {Frontiers of Computer Science},
volume = {16},
number = {2},
pages = {162701},
year = {2022},
url = {https://doi.org/10.1007/s11704-020-0013-1},
doi = {10.1007/s11704-020-0013-1}
}

RCAnalyzer: Visual Analytics of Rare Categories in Dynamic Networks

A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time. Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in the dynamic network, the rare occurrence of which could damage the development of the entire structure. In this study, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verify the effectiveness of our system.

@article{PanHGZCHXC20
title="RCAnalyzer: visual analytics of rare categories in dynamic networks",
author="Jia-cheng Pan, Dong-ming Han, Fang-zhou Guo, Da-wei Zhou, Nan Cao, Jing-rui He, Ming-liang Xu, Wei Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="4",
pages="491-506",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900310"
}

2019

Structure-based suggestive exploration: A new approach for effective exploration of large networks

When analyzing a visualized network, users need to explore different sections of the network to gain insight. However, effective exploration of large networks is often a challenge. While various tools are available for users to explore the global and local features of a network, these tools usually require significant interaction activities, such as repetitive navigation actions to follow network nodes and edges. In this paper, we propose a structure-based suggestive exploration approach to support effective exploration of large networks by suggesting appropriate structures upon user request. Encoding nodes with vectorized representations by transforming information of surrounding structures of nodes into a high dimensional space, our approach can identify similar structures within a large network, enable user interaction with multiple similar structures simultaneously, and guide the exploration of unexplored structures. We develop a web-based visual exploration system to incorporate this suggestive exploration approach and compare performances of our approach under different vectorizing methods and networks. We also present the usability and effectiveness of our approach through a controlled user study with two datasets.

@ARTICLE{ChenGHPNXZ19,
author={Chen, Wei and Guo, Fangzhou and Han, Dongming and Pan, Jacheng and Nie, Xiaotao and Xia, Jiazhi and Zhang, Xiaolong},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Structure-Based Suggestive Exploration: A New Approach for Effective Exploration of Large Networks},
year={2019},
volume={25},
number={1},
pages={555-565},
doi={10.1109/TVCG.2018.2865139}
}

RankBrushers: interactive analysis of temporal ranking ensembles

Temporal ranking ensembles indicate time-evolving multivariate rankings. Such data can be commonly found in our daily life, for example, different rankings of universities (QS, ARWU, THE, and USNews) over year and those of NBA players over season. Effective analysis and tracking of rankings allow users to gain insights into the overall ranking change over time and seek the explanation for the change. This paper introduces a novel visual analytics approach for characterizing and visualizing the uncertainty, dynamics, and differences of ranking ensemble data. A novel visual design is proposed to characterize the evolution pattern, distribution, and uncertainty of a large number of temporal ranking ensembles. The evolutionary ranking ensembles are progressively explored, tracked, and compared by means of an intuitive visualization system. Two case studies and a task-driven user study conducted on real datasets demonstrate the effectiveness and feasibility of the implemented system.

@article{HanPGLWZC19,
author= {Dongming Han and Jiacheng Pan and Fangzhou Guo and Xiaonan Luo and Yingcai Wu and Wenting Zheng and Wei Chen},
title = {RankBrushers: interactive analysis of temporal ranking ensembles},
journal = {J. Vis.},
volume = {22},
number = {6},
pages = {1241--1255},
year = {2019},
url = {https://doi.org/10.1007/s12650-019-00598-x},
doi = {10.1007/s12650-019-00598-x},
}

๐ŸŒ Open Source Contributions

NetV.js

A GPU-based large scale network visualization library.


OGDF.js

A JavaScript graph layout library which encapsulates layout algorithms from OGDF.

๐Ÿ‘จโ€๐Ÿ’ป Work & Research Experience

2018: VIS talk

2020: VIS talk

2019 ~ 2021: Intern of Zhejiang Lab

๐Ÿ† Awards & Scholarships

Master-Ph.D. Period (2017-2022)

Bachelor Period (2013-2017)