Ching Jin

Also Known as, Qing Jin

Assistant Professor

Centre for Interdisciplinary Methodologies
University of Warwick

About me

I am a computational social scientist, who specializes in developing novel computational methodologies by leveraging tools from network science, statistical physics, and artificial intelligence. My work involves applying these methods to large-scale datasets across various domains, including technology, science, and commerce, with the overarching goal of describing, modeling, and predicting social patterns.

My research has resulted in five academic journal publications, including first-authored papers in Nature Human Behaviour (featured on the cover) and Nature Communications. Additionally, my work has been covered in media outlets such as Harvard Business Review and Science Daily.

I am currently an Assistant Professor in Data Science at CIM (Centre for Interdisciplinary Methodologies) at the University of Warwick. I earned my PhD in Physics from Northeastern University, under the supervision of Prof. Dashun Wang and Professor Albert-László Barabási. Following my doctoral studies, I served as a postdoctoral researcher at the Kellogg School of Management and held the role of Associate Director at the Northwestern Institute on Complex Systems (NICO), both at Northwestern University, under the guidance of Professor Brian Uzzi.

Research and Projects

My Research and Projects :

My main research interest lies in understanding complex diffusions in social systems. I bring methodologies from statistical physics, complexity science, network science, and artificial intelligence to provide new directions in innovation studies. Specifically, I develop novel, rigorous mathematical frameworks, network models, and causal inference techniques to explore large-scale datasets in different domains, from technology and science to commerce and medicine, offering new quantitative insights into technology forecasting, sustainable growth, knowledge diffusion, and scientific policy making. These methodologies and approaches also enable me to explore several related questions, such as substitution chains, information diffusion and diversity, equity, and inclusion in social systems, and to predict, sustain, and navigate growth in social systems. Here are a few research projects: 

1.Universal Substitution Patterns

Diffusion processes are central to human interactions. One common prediction of the current modeling frameworks is that initial spreading dynamics follow exponential growth. Here we find that, for subjects ranging from mobile handsets to automobiles and from smartphone apps to scientific fields, early growth patterns follow a power law with non-integer exponents. We test the hypothesis that mechanisms specific to substitution dynamics may play a role, by analyzing unique data tracing 3.6 million individuals substituting different mobile handsets. We uncover three generic ingredients governing substitutions, allowing us to develop a minimal substitution model, which not only explains the power-law growth but also collapses diverse growth trajectories of individual constituents into a single curve. These results offer a mechanistic understanding of power-law early growth patterns emerging from various domains and demonstrate that substitution dynamics are governed by robust self-organizing principles that go beyond the particulars of individual systems.

Main paper
Website
Behind the paper
Media
Science Daily
Phys.org
Harvard Business Review

2. Abandonment of Innovation and Emergence of Fragility in Robust Ecosystems

Despite extensive studies on diffusion of innovations, our knowledge about the reverse process—abandonment of innovations—remains limited. Here, we analyze two large-scale datasets, each capturing detailed socio-temporal patterns that trace the full lifecycle of innovations. By analyzing 2.5M scientists engaging research in 2651 scientific fields and 3.5M individuals using 994 mobile handsets, we find that, in contrast to the Poissonian dynamics commonly assumed in the abandoning process, the abandoning probability increases with the number of past abandonments, suggesting a bandwagon effect characterizing the abandonment of innovations.

We examine the social networks underneath the two systems through co-authorships and mobile communication records, finding that a preferential abandonment mechanism at a network level is responsible for generating the observed effect. Most importantly, we show analytically that the presence of preferential abandonment induces a structural collapse in the topology of the system, where networked systems that were thought to be robust undergo a novel phase transition. We test the theoretical predictions systematically in our datasets, obtaining broadly consistent empirical support. Together these results demonstrate that the collapse of real systems follows reproducible but fundamentally different dynamics than what traditional theoretical frameworks predicted.

Our findings suggest that preferential abandonment and the structural collapse it induces may be a generic property that prevails in the declining phase of the innovation lifecycle.

3. Scientific Prizes and Scientific Growth

Fast growing scientific topics have famously been key harbingers of the new frontiers of science, yet, large-scale analyses of their genesis and impact are rare. We investigated one possible factor connected with a topic’s extraordinary growth: scientific prizes. Our longitudinal analysis of nearly all recognized prizes worldwide and over 11,000 scientific topics from 19 disciplines indicates that topics associated with a scientific prize experience extraordinary growth in productivity, impact, and new entrants. Relative to matched nonprizewinning topics, prizewinning topics produce 40% more papers and 33% more citations, retain 55% more scientists, and gain 37 and 47% more new entrants and star scientists, respectively, in the first five-to-ten years after the prize. Funding does not account for a prizewinning topic’s growth.

Rather, growth is positively related to the degree to which the prize is discipline-specific, conferred for recent research, or has prize money. These findings reveal new dynamics behind scientific innovation and investment.
Main paper
Media
Science Daily
Phys.org
Harvard Business Review

4. Scientific Prizes and Knowledge Diffusion

In the ever-accelerating pace of scientific growth, we witness a vast influx of novel concepts within the academic ecosystem. The integration and connectivity of these emergent ideas present a profound puzzle, which we address by turning our gaze toward the world of scientific recognition—specifically, scientific prizes. Our research meticulously curates a comprehensive dataset of thousands of scientific awards, constructing a network that maps the intricate hierarchical relationships between them. We delve into the quantification of this structure, scrutinizing the patterns of transition and succession to unveil the underlying architecture of idea integration within science. Through our investigation, we reveal how scientific prizes not only celebrate but also significantly contribute to the confluence of new ideas, offering a unique lens to observe the fusion of scientific thought.

This study thus uncovers the pivotal role of scientific accolades in shaping the trajectory of ideas and their interconnectivity, charting the course of scientific evolution and the burgeoning spread of knowledge across the global scientific community.

5. Diversity, Equity, and Inclusion (DEI) in Science

Diversity, Equity, and Inclusion (DEI) are vital for increasing and sustaining creativity in scientific research. In this study, we conduct a comprehensive analysis of gender disparity within the field of economics, utilizing a large-scale dataset encompassing 2 million publication records and the professional interactions of over 900,000 economists. Our analysis reveals significant underrepresentation of women among prize winners and in important roles such as journal editors. The data suggest that this imbalance may be attributed to biased collaboration, resource allocation, and gender-based homophily in networking. Furthermore, we construct a mathematical modeling framework to link Diversity, Equity, and Inclusion in the academic system. Our findings highlight persistent gender biases and offer insights into their possible origins, providing a substantive foundation for policy implications aimed at fostering diversity, equity, and inclusion in science.