Graph summarisation and compression techniques aim to reduce the size and complexity of large‐scale networks while preserving essential structural and functional information. Summarisation condenses ...

Graph databases offer a more efficient way to model relationships and networks than relational (SQL) databases or other kinds of NoSQL databases (document, wide column, and so on). Lately many ...

Part I of our series on graph analytics introduced us to graph analytics, and its brethren graph databases. We talked about the use of graph analytics to understand and visualize relationships between ...

Graph-based image segmentation frames an image as a weighted graph in which pixels or regions correspond to nodes, and edges encode similarity or affinity between neighbouring elements. By formulating ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...