MAGE 1.2 - Meet Temporal Graph Networks and Dynamic Graph Analytics

Memgraph's MAGE 1.2 release brings significant enhancements to its graph analytics library, introducing both dynamic and static graph algorithms and marking its foray into graph machine learning. This update features new dynamic algorithms—Temporal Graph Networks, Dynamic Betweenness Centrality, and Dynamic Katz Centrality—designed to improve computation efficiency by focusing on updated graph sections rather than recalculating entire graphs. Temporal Graph Networks, powered by PyTorch, enable deep learning on dynamic graphs, offering capabilities like handling node and edge updates for predictive tasks. Additionally, the library expands its static algorithm offerings with Louvain community detection, maximum flow calculation, and static Katz centrality. The release also improves utility functions for importing and exporting graphs using JSON, with plans for broader format support in future updates. Looking ahead, Memgraph plans to enhance scalability with GPU algorithms, introduce custom functions via a C API, and continue integrating machine learning into graph analytics.