Research topic focus
Two central pathways form the core of this study:
- Programming language agnosticism: Crafting a strategy that facilitates the integration of prominent GNN libraries, including but not limited to, Pytorch Geometric, Deep Graph Library, Graph Nets, and Spektral into the Java-centric world of Magma. Within this domain, tools such as Apache Arrow will be evaluated to potentially aid the symbiotic functioning of different programming languages.
- Java-native GNNs exploration: Venturing into the Java landscape to discern the viability of embedding a foundational set of GNNs. This exploration, while promising, remains an additional aspiration rather than an obligatory requirement.
Expected results and learning outcome
- Magma evolution: Successful amalgamation of one or more GNN libraries within the Magma environment.
- Harmonizing programming languages: Insights into tools, notably Apache Arrow, which might bridge the gap between Java and other languages seamlessly.
- GNNs in Java: If viable, a prototype or a foundational version of a GNN conceptualized directly within Java.
- Academic enrichment: A multi-dimensional understanding of data stream-processing, ranging from Java-driven algorithms to diverse GNN libraries.
- Scientific publication: A possible outcome will be the formulation of the results into a research paper for a scientific conference or journal. We will actively support and guide you throughout the scientific writing process, providing a unique opportunity for you to author a scientific paper.
Qualifications
Interested candidates should bring forth a strong Java programming foundation, coupled with an affinity towards graph processing. Previous experiences or insights into neural networks, especially GNNs, would provide an edge. A genuine curiosity, combined with a willingness to navigate the complexities of marrying disparate programming paradigms, stands as an invaluable trait.
Benefits of association with the Graph-Massivizer project
Engaging with the Graph-Massivizer project will grant you an opportunity to delve into avant-garde research and collaborate with a network of distinguished industry affiliates. The project, a beacon of innovation, affords a prestigious platform for both academic growth and professional outreach. Learn more about the project at: https://graph-massivizer.eu/the-project/
Application process and expectations
Once you manifest your interest, you'll be cordially invited for a detailed discussion. To prepare:
- Shallow Dive into GNN libraries: Acquaint yourself with the nuances of libraries such as Pytorch Geometric and Deep Graph Library.
- Understand cross-language integration tools: Delve into platforms like Apache Arrow to fathom their potential in harmonizing Java with other programming dialects.
- Java proficiency: Revisit or enhance your Java skill set, especially honing in on data structures and intricate algorithms.
While an exhaustive comprehension isn't mandatory, a rudimentary understanding will be emblematic of your dedication and will align you more congruently with the project’s ambitions
Contacts
- Daniel Thilo Schroeder, SINTEF, email: daniel.t.schroeder@sintef.no
- Brian Elvesæter, SINTEF, email: brian.elvesater@sintef.no
- Carsten Griwodz (as internal supervisor)