Master's Thesis Opportunity - Spring of 2027

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About Neo4j:

Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy-to-deploy graph capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world’s largest graph community. Intelligence that works. Results that matter.

Built to work everywhere and integrate with everything across every cloud for dynamic, personalized, and autonomous AI systems. We deliver quicker results, contextual knowledge, and solutions that impact customers and employees across the business.

Our Vision:

At Neo4j, we have always strived to help the world make sense of data.

As business, society and knowledge become increasingly connected, our technology promotes innovation by helping organizations to find and understand data relationships. We created, drive and lead the graph database category, and we’re disrupting how organizations leverage their data to innovate and stay competitive.

The Role:

Are you at the end of your studies and want to immerse yourself in graph technology? We are now looking for students who want to do their Master’s Thesis alongside us at Neo4j!
As part of Neo4j engineering in Malmö, you will work with a diverse team of talented colleagues worldwide. You will receive advice and continuous support from us - we are experts in graph technology and positioned to help you perform to the best of your ability.

Past Thesis Topics:

Force Directed Drawing Algorithms and Parameter Optimisation:
Through my thesis I have implemented and compared some different graph drawing algorithms in addition to some methods to speed up the slow parts of these algorithms. These algorithms were then used to test what to the best of my knowledge is a novel approach to select parameter values for graph drawing algorithms. For this, I use methods similar to those used in Machine Learning to select parameter values and measure the utility of any set of parameters by creating my own utility function. I created this function by looking at objective measures of drawing quality that are commonly known, such as the number of edge crossings, along with the time it took to draw a given graph. The resulting method for parameter optimisation could find significant increases in the speed of graph drawing for several of my implemented drawing algorithms without compromising drawing quality. Furthermore, the approach is not specific to any parameter set, and can with some modification be applied to any graph drawing algorithm dependent on some constants.

Modeling Profiling Data in a Graph Database for Performance Analysis:
Benchmarking is an important part of the development process for any mission-critical application. By inspecting profiling data, developers can identify bottlenecks and performance regressions before they reach the customers.

Neo4j runs an extensive benchmarking suite on its database, resulting in a huge collection of profiling data collected each week. These profiles are commonly visualized individually as flame graphs which are inspected manually. Finding patterns and differences among multiple profiles is difficult to do manually, due to the size and complexity of the data. We propose a framework for identifying bottlenecks and regressions by modeling the profiling data as call-stack trees in a graph database. We demonstrate the usefulness of the framework for cross-profile analysis such as time series analysis and aggregation-based methods. We conclude that there is much potential in this approach and our thesis can be used as a decision basis for organizations wanting to implement a similar framework.
Using a graph database to model profiling data has many advantages and is suitable for the tree-like structure of the data. It makes the data more accessible and facilitates flexible querying in which the user can ask questions about the data and perform non-trivial aggregation. It has already aided Neo4j in the process of pinpointing the cause of some performance issues. The main disadvantage is the complexity involved in importing large quantities of data.

Navigating Failures in Distributed Systems: A Comparative Study of Failure Detection Algorithms:
Failure detection algorithms are used to identify unhealthy nodes in distributed systems. The goal of this study was to improve Neo4j’s use of failure detection algorithms by exploring two paths: either optimising their existing Lighthouse algorithm or by implementing a new algorithm. Existing algorithms were surveyed and the SWIM algorithm was implemented. A baseline was established and evaluated against parameter-optimized versions of SWIM and Lighthouse in a simulated network. The results show that Baseline is scalable and reliable but slow, Lighthouse is fast but less accurate, and SWIM is moderately fast and the least accurate but generates the least network load. In conclusion, the chosen parameters of a failure detector are to a great extent more important than the algorithm itself. Furthermore, to successfully optimise parameters it is crucial to have a scalable simulator and precise system requirements to manage the trade-off between speed, accuracy, and network load.

Cache replacement policies and their impact on graph database operations:
In this master thesis project, the page caching strategy of the Neo4j database is researched and attempted to be improved. Focusing on the eviction protocol of the page cache, several different algorithms are evaluated in both experimental prototyping using Python, and in the Neo4j database kernel. Using the measurements of the prototypes and the results of the Neo4j benchmarks conclude that the current page replacement policy is hard to beat with a different strategy. However, modifying the current page…