Interview with Dr. Ranran Wang—Winner of the Symmetry Best PhD Thesis Award
We are pleased to announce that Dr. Ranran Wang has won the 2024 Best PhD Thesis Award. As the winner, she will receive CHF 500, a certificate and a free voucher for article processing fees valid for one year for Symmetry (IF: 2.2, ISSN: 2073-8994).
Dr. Wang’s awarded thesis, titled “Research on Network Diffusion Mechanisms and Applications Based on the Minimal Substitution Model”, was completed under the direction of Prof. Dr. Yin Zhang. Dr. Wang is now completing postdoctoral work at Sun Yat-sen University to continue studying information diffusion and graph mining.
The following is an interview with Dr. Wang:
1. Could you introduce your PhD research and the main objectives of your doctoral dissertation?
My doctoral research focused on the “Minimal Substitution Theory” in computational science, with a particular emphasis on explaining and leveraging the fundamental principles underlying information diffusion. Through the integration of machine learning techniques, I investigated methods for simulating information diffusion in networks and its practical applications. Early in my PhD, I came across the “Minimal Substitution Theory” in Nature. Unlike traditional information diffusion theories, this theory models information spread as a competitive process in which different pieces of information vie for dominance — a concept that I found deeply captivating. This theory closely aligns with everyday intuition. For example, a social media user has limited attention. If they spend an hour intently watching a talk show, they will likely have no capacity left for a documentary such as Animal World. From an information diffusion perspective, the talk show and Animal World are in direct competition for attention during that hour, and such competition between pieces of information is ubiquitous across information systems. My doctoral research was based on the core hypothesis that most information diffusion in the real world follows the Minimal Substitution Theory. Building on this, my research focused on integrating relevant knowledge and techniques from machine learning to develop novel methods and models. The research aimed to solve two key problems: first, to accurately predict the development trends of information diffusion; and second, to scientifically model the complete process of information diffusion. These research components also constituted the main objectives of my doctoral dissertation, which aimed to provide new perspectives and technical support for understanding and utilizing the laws of information dissemination.
2. What was the biggest challenge you faced while pursuing your PhD, and how did you overcome it?
The biggest challenge I faced during my doctoral studies arose in my third year. Several papers that I had submitted and revised multiple times were rejected over a concentrated period. This made me strongly doubt my academic capabilities. However, I chose to confront the problem directly. I changed my mindset by taking part in a joint training program abroad. After my state improved, I revised the previously rejected research and resubmitted it. Ultimately, these papers were accepted successively within the following year.
3. In your opinion, what key qualities should an excellent PhD graduate possess? Do you have some advice for doctoral students who have not yet graduated? Many doctoral students experience pressure and worry about their graduation being delayed.
In my opinion, the three most important qualities for PhD students are curiosity, perseverance and a balanced mindset. Curiosity is at the heart of research, prompting you to ask questions and explore the unknown. Perseverance is reflected in how one deals with frequent failures — it's about being adept at summarizing experiences and seeking clues that lead to success rather than dwelling on the failures themselves. Having a balanced mindset means finding the right balance between study and life. It's important to have the courage to push forward in your research, but also to know when to take a step back and pay attention to your physical and mental health.
My advice to current doctoral students is not to measure your self-worth by how quickly you graduate. The doctoral stage is a process of exploring knowledge and pushing cognitive boundaries. Everyone’s research is fraught with difficulties. Graduating more slowly or facing delays does not signify failure. In fact, the challenges and difficulties encountered along the way better illustrate the value of this educational journey. There is no need to worry excessively about graduation timelines.
4. Before this, were you aware of the journal Symmetry, and have you been following it? What is your general impression of it?
I first became aware of Symmetry through an introduction by my supervisor, Prof. Yin Zhang, who is an editorial board member of Symmetry. I consider Symmetry to be a high-quality journal, and I am impressed by its multidisciplinary nature. I am also grateful to the Symmetry journal for this honor.
5. As an author, what aspects of a journal do you value the most when choosing a journal in which to publish your academic work? Symmetry is an open access journal—what are your thoughts on the open access publishing model?
When selecting a journal for publishing my academic results, I prioritize the journal's influence, the fairness of the peer-review process and whether it allows my research findings to be openly displayed. I also consider whether the research will generate positive value within my field, and I value the journal's open access attributes. I believe the open access publishing model is highly significant. It gives more people the chance to read various research findings, which aligns with the essence of open science, promoting communication and connection. It can also inspire others and foster innovation. Therefore, I highly appreciate and align with Symmetry's open access policy.
6. What are your future research plans, and what are your long-term career goals?
My current research interests lie in delving deeper into research related to network information diffusion mechanisms. Building on my previous work, I intend to extend the focus to utilizing these mechanisms to empower graph models with reasoning capabilities. I believe that graphs are a widely present data structure in the real world and that enabling graph models to possess reasoning abilities is of great importance. Regarding the current trend of using large language models (LLMs) directly for graph reasoning, I note that LLMs have limitations when handling complex graph data. Their reasoning and computation processes can be complex, and their understanding of graph structures is often insufficient. Therefore, my core research approach is to start with the graph data itself, combining it with the information diffusion mechanisms I have previously studied to explore new ways of equipping graph models with reasoning capabilities. My long-term career goal is to remain in academia, continuing scientific research and pursuing sustained, in-depth study in this field.
7. As the recipient of this award, could you share your feelings and whom you would like to thank?
As the recipient of this award, I am deeply honored and grateful. This recognizes not only my research work during my doctoral studies, but also the numerous challenges that I have overcome in the past. I would especially like to thank the many individuals who supported me, including Prof. Yin Zhang, who provided invaluable guidance during my master’s and PhD; Prof. Yan Zhang, who offered me academic and professional advice; Prof. Henning Meyerhenke, who guided me during my joint training period in Germany; my collaborators and the members of my research group during my PhD; and my family and friends, who have always supported and encouraged me, and helped me through difficult times.