Graph Massivizer: Efficient Algorithms for Processing and Analyzing large-scale Graphs

Graphs are widely used to model complex systems such as social networks, transportation networks, and biological systems. However, as the size of these graphs grows, processing and analyzing them becomes increasingly challenging. This is where Graph-Massivizer comes in. This research project aims to develop algorithms that can efficiently handle massive graphs with billions of vertices and edges.


Graph-Massivizer is a research project aimed at developing efficient algorithms for processing and analyzing large-scale graphs and developing innovative products, particularly for use cases, that can be commercialized after the project ends. Furthermore, the project aims to create an integrated platform that is user-friendly and easy to deploy in enterprise environments, using the metaphactory knowledge graph platform as a basis. The platform will tightly integrate the tools developed by Graph-Massivizer to provide a comprehensive offering. This project is a collaboration between researchers from various institutions across Europe and is funded by the European Union's Horizon 2020 program.


As part of the Graph-Massivizer project, metaphacts will focus on graph compression, graph partitioning, and graph algorithms. Graph compression aims to reduce the size of large graphs without losing important information, allowing them to be stored and processed more efficiently. Graph partitioning involves dividing a graph into smaller subgraphs to enable parallel processing and improve performance. Finally, we are developing novel graph algorithms that can efficiently operate on massive graphs.