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 Bachelor’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.
- Personal Page: http://panjiacheng.site/
- Github: https://github.com/JackieAnxis
- Visual Analytics Group of ZJU: https://zjuvag.org/
Major: Visualization and Visual Analytics
- Mentor: Professor Wei Chen
- Lab: State Key Lab of CAD&CG
- College: College of Computer Science and Technology
Major: Software Engineering
Minor: Intensive Training Program of Innovation and Entrepreneurship
- College: Chu Kochen Honors College
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.
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.
- Authors: Dongming Han, Jiacheng Pan, Cong Xie, Xiaodong Zhao and Wei Chen
- Publication: IFAC-PapersOnLine
- Authors: Dongming Han, Jiacheng Pan, Xiaodong Zhao and Wei Chen
- Publication: Visual Informatics
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.
- Authors: Jiacheng Pan, Wei Chen, Xiaodong Zhao, Shuyue Zhou, Wei Zeng, Minfeng Zhu, Jian Chen, Siwei Fu, Yingcai Wu
- Publication: IEEE Transactions on Visualization and Computer Graphics
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.
- Authors: Dongming Han, Jiacheng Pan, Rusheng Pan, Dawei Zhou, Nan Cao, Jingrui He, Mingliang Xu, Wei Chen
- Publication: Frontiers of Computer Science
Multivariate 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.
- Authors: Jiacheng Pan, Dongming Han, Fangzhou Guo, Dawei Zhou, Nan Cao, Jingrui He, Wei Chen
- Publication: Frontiers of Information Technology & Electronic Engineering
Rare Category Detection
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.
- Authors: Wei Chen, Fangzhou Guo, Dongming Han, Jiacheng Pan, Xiaotao Nie, Jiazhi Xia, Xiaolong Zhang
- Publication: IEEE Transactions on Visualization and Computer Graphics
Large Network Exploration
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.
- Authors: Dongming Han, Jiacheng Pan, Fangzhou Guo, Xiaonan Luo, Yingcai Wu, Wenting Zheng, Wei Chen
- Publication: Journal of Visualization
Temporal Ranking Ensembles
A GPU-based large scale network visualization library.
- Mainly developed using
- Providing customized APIs for graph data
- Corresponding to plenty of basic interaction events
- Providing unified APIs for different layout algorithms
- Containing penlenty of different layouts (in progress)
- Supporting web worker
- Presenting Structure-Based Suggestive Exploration at IEEE VIS 2018 with Dongming Han
- Presenting Exemplar-based Layout Fine-tuning for Node-link Diagrams at IEEE VIS 2020 (online)
2019 ~ 2021: Intern of Zhejiang Lab
- Mainly responsible for a large scale network visualization project.
- Freshmen Scholarship of Zhejiang University
- Jiang Zhen Scholarship of Zhejiang University
- Certificate of Zhejiang University Academic Star
- Gold Award of the Second China College Students’ Entrepreneurship Competition
- Silver Award of the Second China “Internet+” College Students Innovation and Entrepreneurship Competition
- Second-Class Scholarship for Outstanding Merits of Zhejiang University
- Certificate of Excellence Engineer Training Program
- Leadership and Service Star Awards of College of Computer Science and Technology
- Zhejiang Daily & Alibaba New Media Scholarship