Can Data Reveal How Communities Grow? Inside Parul University’s Research on Collaboration and Influence

We tend to measure people by individual achievements. But what if the deeper story of how someone grows lies not in the individual, but in the web of relationships around them? That is the question behind a new research paper by Dr. Sanjay Agal, Head of the Department of Artificial Intelligence and Data Science at Parul University, titled “A Multiplex Graph Neural Network Framework for Educational Collaboration and Influence Analytics: A Case Study,” published in Scientific Reports, a journal of the Nature Portfolio. It adds to the department’s growing record of research in the journal.

The idea began with an observation. We interact with hundreds of people, and many exchanges seem insignificant at the time, a chat about travel, a book, a passing experience. Yet, as Dr. Agal points out, a casual conversation with someone who once visited a place can become genuinely useful years later when you plan a similar journey. His research explores this idea of hidden influence: how interactions and relationships create effects that are not immediately visible but grow meaningful over time. The same principle applies to academic life, where a faculty member’s collaborations, with colleagues, with researchers in other cities, with authors elsewhere, quietly shape their research opportunities and development.

The paper’s central move is to treat collaboration not as a simple list of co-authors, but as a network. If a Parul University researcher publishes with authors from Pune and Noida, those links become part of a larger academic network; gather many such collaborations across people, cities, and organisations, and you can visualise the collaboration structure of an entire department. Using graph neural networks (a form of AI designed for exactly this kind of connected data) and bibliometric records, the study maps these multi-relational networks and examines a fascinating question: does a researcher’s position within the collaboration network relate to their academic outcomes? In other words, could your environment and connections shape what you achieve?

Rather than study an abstract dataset, Dr. Agal turned the lens on Parul University itself. The university’s scale, a large faculty community, strong infrastructure, and institutional support from the research department, Dean, and management, made it a substantial environment for studying collaboration and influence. The AIDS Department’s own faculty research activity and collaboration patterns became the basis of the case study. It is a striking idea: an AI and Data Science research question, explored using the real academic data of the very institution conducting it.

Predicting human behaviour, though, is not like predicting a machine. As Dr. Agal emphasised, people can change unexpectedly: historical data may show one pattern, but a single new decision can redirect it entirely. For that reason, the research does not claim to predict human behaviour with certainty. Instead, it explores patterns within networks and the influence of meaningful connections, treating humanity itself as one of the great challenges of behavioural data science. It is a refreshingly honest stance: the goal is insight into structure and possibility, not a crystal ball.

One of the most resonant ideas to emerge is the difference between many connections and meaningful ones. In a digital world, a person may have thousands of online followers, yet numbers say little about the strength or usefulness of those relationships. Dr. Agal stresses selective, meaningful influence: choosing collaborations that genuinely advance learning, research, and growth. In an academic setting, that means working with people who encourage research, share expertise, and open doors, and the effect is often gradual, someone develops a new research interest because of the people around them, without at first recognising the source..

Asked what research means to him, Dr. Agal’s answer was disarmingly simple: “Happiness.” Reading people, reading books, and talking with others give him ideas to write; writing itself brings satisfaction, with publication a separate outcome. That openness shows in his cross-disciplinary work, including helping pharmacy faculty develop a Python programming book for B.Pharm students. It also shapes his future plans: he aims to create and freely share very large datasets (he described a financial-technology dataset of some 256 GB, offered free for research), so that researchers everywhere can experiment without being limited by expensive or inaccessible data.

The larger message reaches beyond one department. The research shows how a university’s own academic ecosystem can become the foundation for work that engages global conversations, and Dr. Agal was explicit that the AIDS Department is keen to build more international collaborations with leading universities and researchers worldwide. Parul University, he stressed, is not only seeking opportunities abroad but positioning itself as an institution with the researchers, infrastructure, and capabilities to contribute to international projects. It is an open invitation: collaboration can move in both directions.

For anyone beginning research, Dr. Agal’s advice is practical: read, but do not try to read everything. Narrow your search by time and domain, starting, say, with recent work from the current year in your specific field, to understand what the world is working on now, what approaches exist, and where the gaps are. He likens it to music: before creating something new, a musician must understand what already exists, what instruments are used, and what others are making. Research follows the same principle, you must understand the landscape before you can meaningfully add to it.