Complex network representing scientific innovation over a DNA double helix.

Decoding Innovation: Can We Predict the Next Breakthrough?

Rhea Morgan in Tech & Innovation July 2025 • 4 min read.

"A new study combines complexity science and big data to reveal hidden patterns in how innovations, like vaccines, emerge and accelerate."

We often think of innovation as a forward march, but the path to groundbreaking discoveries is rarely a straight line. It's a messy process, full of dead ends, unexpected turns, and collaborations that span years. But what if we could understand the 'order' hidden within this complexity? What if we could identify the key steps that truly drive innovation forward?

A recent study is offering a new approach to the challenge, one that moves beyond simple timelines and delves into the intricate relationships between scientific advancements. By combining complexity science with the vast amounts of data available on vaccine development, researchers are uncovering the underlying patterns that govern how innovations actually happen.

This isn't just an academic exercise. Understanding the 'order of innovation' could revolutionize how we fund research, design innovation programs, and ultimately, accelerate the development of life-saving technologies.

Beyond the Timeline: Mapping the Innovation Network

Complex network representing scientific innovation over a DNA double helix.

The traditional way of looking at innovation is to chart events on a linear timeline. However, this approach struggles to capture the true essence of how innovations build upon each other. It also fails to identify the critical bottlenecks that slow down progress. Imagine a multi-step chemical reaction: the overall process can only move as fast as its slowest, rate-determining step. Similarly, the innovation process contains intermediary outputs, and it has bottlenecks whose catalysis could accelerate overall innovation progress.

To address this limitation, the study's authors propose a novel approach, the use of a multilayer directed acyclic graph (DAG). Think of a DAG as a map where each node represents a scientific or technological milestone—a research paper, a patent, a clinical trial—and the edges connecting the nodes show how one advancement builds upon another through citations. This network visually represents the flow of knowledge and influence, revealing the order in which innovations occur. It is helpful to know: a network (or graph) is a set of nodes, and pairs of nodes can be connected by an edge.

Nodes: Each node represents a single document which is one of four types: an innovation outcome represented by regulatory authorisation, a clinical trial, a patent, or an academic publication.
Edges: The edges, written as (u, v), are citations from one node u to another node v; citation networks have a sense of direction as (u, v) represents an entry listing document v in the bibliography of a document u, not the other way round.
DAG structure: The data gives networks where 0.07% of all edges are part of a cycle, for example, due to authors citing each others' paper during journal submission or mislabelling. This 0.07% is removed to to ensure reduce our the networks we analyse are always DAGs.

By analyzing the structure of this network, researchers can identify 'critical paths'—sequences of events that are most crucial for driving innovation forward. They can also pinpoint bottlenecks, areas where progress is slow or stagnant. This allows for a more nuanced understanding of the innovation process than a simple timeline could ever provide.

From Data to Action: Shaping the Future of Innovation

This new approach to understanding innovation is more than just a theoretical exercise. By revealing the hidden order within complex systems, it offers a powerful tool for policymakers, researchers, and industry leaders alike. Imagine being able to identify the most promising areas for research investment, anticipate potential bottlenecks, and accelerate the development of life-saving technologies. By studying the series of vaccine citation networks, we can discover many different things that will help build medical products quicker.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

Everything You Need To Know

What is the main problem with using linear timelines to understand innovation?

Linear timelines struggle to capture the true essence of how innovations build upon each other and fail to identify the critical bottlenecks that slow down progress. Unlike the interconnected nature of scientific advancements, a linear view simplifies a complex process, missing the nuances of collaboration, dead ends, and iterative development. Traditional timelines don't account for intermediary outputs or the 'order' in which innovations occur, which is crucial for understanding the entire process.

How does the study use a multilayer directed acyclic graph (DAG) to map the innovation process?

The study uses a multilayer directed acyclic graph (DAG) to visually represent the flow of knowledge and influence within the innovation process. In this graph, nodes represent scientific or technological milestones, such as research papers, patents, and clinical trials, while edges show how one advancement builds upon another through citations. By analyzing the structure of this network, researchers can identify 'critical paths' and bottlenecks, offering a more nuanced understanding than simple timelines.

What are the key components of a DAG in the context of this study, and how do they function?

In this study's DAG, there are key components that help map the innovation process. Nodes represent a single document, classified into one of four types: regulatory authorization, clinical trial, patent, or academic publication. Edges, written as (u, v), represent citations from one node u to another node v. These citations have a sense of direction as (u, v) lists document v in the bibliography of document u. The data gives networks where a very small percentage of edges are part of a cycle, and these are removed to ensure the networks are always DAGs.

How can understanding the 'order of innovation' revolutionize the development of technologies?

Understanding the 'order of innovation' could revolutionize technology development by enabling the identification of the most promising areas for research investment. It allows us to anticipate potential bottlenecks and accelerate the development of life-saving technologies. By pinpointing the 'critical paths' and understanding the relationships between scientific advancements, we can optimize resources, improve the efficiency of the innovation process, and ultimately bring new technologies to market faster.

What are the practical implications of applying complexity science and big data to understand innovation?

Applying complexity science and big data to understand innovation offers practical implications for various stakeholders. Policymakers can make informed decisions about research funding, directing resources to areas with the highest potential impact. Researchers can gain insights into the most effective strategies for collaboration and knowledge dissemination. Industry leaders can identify emerging trends, anticipate market needs, and streamline their product development processes. Ultimately, this approach can lead to the faster development of life-saving technologies and other beneficial innovations.