Triangle counting stands as a fundamental task in graph theory and network science, offering critical insights into the structural properties of complex systems. By enumerating all sets of three ...

Graph Neural Networks (GNNs) have gained widespread adoption in recommendation systems. When it comes to processing large graphs, GNNs may encounter the scalability issue stemming from their ...

In recent years, knowledge graphs have become an important tool for organizing and accessing large volumes of enterprise data in diverse industries — from healthcare to industrial, to banking and ...

If you are interested in learning how to build knowledge graphs using artificial intelligence and specifically large language models (LLM). Johannes Jolkkonen has created a fantastic tutorial that ...

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

Victor Lee is director of product management at TigerGraph. Graph databases excel at answering complex questions about relationships in large data sets. But they hit a wall—in terms of both ...

David Conlon and Asaf Ferber have raised the lower bound for multicolor “Ramsey numbers,” which quantify how big graphs can get before patterns inevitably emerge. “There is no absolute randomness in ...

Neo4j is both the original graph database and the continued leader in the graph database market. Designed to store entities and relationships, and optimized to perform graph operations such as ...

If open source is the new normal in enterprise software, then that certainly holds for databases, too. In that line of thinking, Github is where it all happens. So to have been favorited 10.000 times ...