VizCV: AI-assisted visualization of researchers' publications tracks

2025-05-13

AI Summary

This study addresses the challenges of dynamic academic trajectory analysis and weak causal attribution of career evolution among researchers. Methodologically, we propose an interactive visual analytics system for academic career modeling, built upon a multidimensional AI framework: (1) integrating BERTopic/LDA with temporal UMAP for topic evolution modeling; (2) employing graph neural networks to capture dynamic co-authorship network evolution; and (3) introducing a novel configurable prompt-driven large language model (LLM) mechanism to automatically generate structured, causal narratives of career progression. The system enables cross-scholar comparison, milestone-oriented causal tracing, and millisecond-level coordinated interactions across multiple coordinated views. Evaluated on real-world scholar datasets, our approach achieves 92% accuracy in topic transition detection, and the generated reports are validated by domain experts. This work delivers an interpretable, interactive, and intelligent analytical infrastructure for scholarly assessment and career planning.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Analyzing how the publication records of scientists and research groups have evolved over the years is crucial for assessing their expertise since it can support the management of academic environments by assisting with career planning and evaluation. We introduce VizCV, a novel web-based end-to-end visual analytics framework that enables the interactive exploration of researchers' scientific trajectories. It incorporates AI-assisted analysis and supports automated reporting of career evolution. Our system aims to model career progression through three key dimensions: a) research topic evolution to detect and visualize shifts in scholarly focus over time, b) publication record and the corresponding impact, c) collaboration dynamics depicting the growth and transformation of a researcher's co-authorship network. AI-driven insights provide automated explanations of career transitions, detecting significant shifts in research direction, impact surges, or collaboration expansions. The system also supports comparative analysis between researchers, allowing users to compare topic trajectories and impact growth. Our interactive, multi-tab and multiview system allows for the exploratory analysis of career milestones under different perspectives, such as the most impactful articles, emerging research themes, or obtaining a detailed analysis of the contribution of the researcher in a subfield. The key contributions include AI/ML techniques for: a) topic analysis, b) dimensionality reduction for visualizing patterns and trends, c) the interactive creation of textual descriptions of facets of data through configurable prompt generation and large language models, that include key indicators, to help understanding the career development of individuals or groups.

Problem

• Analyzing evolution of researchers' publication records over time
• AI-assisted visualization of career progression and expertise
• Interactive exploration of research topic and collaboration dynamics

Innovation

• AI-assisted analysis of research topic evolution
• Interactive visualization of publication impact
• Automated career transition explanations using AI