# Forecasting high-impact research topics via machine learning on evolving knowledge graphs

arXiv: https://arxiv.org/abs/2402.08640 · HTML: https://mlsanity.com/paper/2402.08640/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 69 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020)
- [On scientific understanding with artificial intelligence](https://mlsanity.com/paper/2204.01467/) (2022)
- [The physics of higher-order interactions in complex systems](https://mlsanity.com/paper/2110.06023/) (2021)
- [Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network](https://mlsanity.com/paper/2210.00881/) (2022)
- [Quantifying Long-Term Scientific Impact](https://mlsanity.com/paper/1306.3293/) (2013)
- [Accelerating science with human-aware artificial intelligence](https://mlsanity.com/paper/2306.01495/) (2023)
- [Tradition and Innovation in Scientists' Research Strategies](https://mlsanity.com/paper/1302.6906/) (2013)
- [Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics](https://mlsanity.com/paper/1906.06843/) (2019)
- [The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles](https://mlsanity.com/paper/2104.00948/) (2021)
- [Defining and identifying Sleeping Beauties in science](https://mlsanity.com/paper/1505.06454/) (2015)
- [Predicting the long-term citation impact of recent publications](https://mlsanity.com/paper/1503.09156/) (2015)
- [Detecting signal from science:The structure of research communities and prior knowledge improves prediction of genetic regulatory experiments](https://mlsanity.com/paper/2008.09985/) (2020)
- [H2CGL: Modeling Dynamics of Citation Network for Impact Prediction](https://mlsanity.com/paper/2305.01572/) (2023)
- [An Overview on Evaluating and Predicting Scholarly Article Impact](https://mlsanity.com/paper/2008.03867/) (2020)
- [Predicting Research Trends in Artificial Intelligence with Gradient Boosting Decision Trees and Time-aware Graph Neural Networks](https://mlsanity.com/paper/2201.05743/) (2022)

## Built on by (most cited first, 22 of 22)

- [Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context](https://mlsanity.com/paper/2412.17596/) (2024)
- [Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders](https://mlsanity.com/paper/2405.17044/) (2024)
- [Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data](https://mlsanity.com/paper/2402.12391/) (2024)
- [Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models](https://mlsanity.com/paper/2505.14599/) (2025)
- [Learning to Predict Future-Aligned Research Proposals with Language Models](https://mlsanity.com/paper/2603.27146/) (2026)
- [Towards autonomous quantum physics research using LLM agents with access to intelligent tools](https://mlsanity.com/paper/2511.11752/) (2025)
- [Discovering emergent connections in quantum physics research via dynamic word embeddings](https://mlsanity.com/paper/2411.06577/) (2024)
- [Semantic Calibration Prevails Where Token Confidence Fails: Benchmarking Long-Form Scientific QA](https://mlsanity.com/paper/2602.00279/) (2026)
- [In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis](https://mlsanity.com/paper/2505.14838/) (2025)
- [FOS: A Large-Scale Temporal Graph Benchmark for Scientific Interdisciplinary Link Prediction](https://mlsanity.com/paper/2511.18631/) (2025)
- [SciImpact: A Multi-Dimensional, Multi-Field Benchmark for Scientific Impact Prediction](https://mlsanity.com/paper/2604.17141/) (2026)
- [Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation](https://mlsanity.com/paper/2605.21491/) (2026)
- [ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment](https://mlsanity.com/paper/2606.00644/) (2026)
- [ELMM: Efficient Lightweight Multimodal Large Language Models for Multimodal Knowledge Graph Completion](https://mlsanity.com/paper/2510.16753/) (2025)
- [Challenge-Device-Synthesis: A multi-disciplinary approach for the development of social innovation competences for students of Artificial Intelligence](https://mlsanity.com/paper/2405.19243/) (2024)
- [LLM-Metrics: Measuring Research Impact Through Large Language Model Memory](https://mlsanity.com/paper/2605.22176/) (2026)
- [Explainable Forecasting of Scientific Breakthroughs from Concept Network Dynamics](https://mlsanity.com/paper/2606.03864/) (2026)
- [Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing](https://mlsanity.com/paper/2606.03919/) (2026)
- [RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases](https://mlsanity.com/paper/2609.10092/) (2026)
- [BackTrend: Evaluating Scientific Weak-Signal Prediction via Backward Reconstruction](https://mlsanity.com/paper/2609.24921/) (2026)
- [Learning to Ideate for Scientific Impact](https://mlsanity.com/paper/2609.29802/) (2026)
- [Historical Anchors for Scientific Forecasting on Human-Aware Knowledge Graphs](https://mlsanity.com/paper/2610.14023/) (2026)

## Abstract (arXiv)

The exponential growth in scientific publications poses a severe challenge for human researchers. It forces attention to more narrow sub-fields, which makes it challenging to discover new impactful research ideas and collaborations outside one's own field. While there are ways to predict a scientific paper's future citation counts, they need the research to be finished and the paper written, usually assessing impact long after the idea was conceived. Here we show how to predict the impact of onsets of ideas that have never been published by researchers. For that, we developed a large evolving knowledge graph built from more than 21 million scientific papers. It combines a semantic network created from the content of the papers and an impact network created from the historic citations of papers. Using machine learning, we can predict the dynamic of the evolving network into the future with high accuracy (AUC values beyond 0.9 for most experiments), and thereby the impact of new research directions. We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.