Katana Graph is developing the next generation of graph computing
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Katana Graph Performance

Katana Graph derives performance advantages from innovations graph algorithms, storage technologies, and hardware acceleration. The Katana Graph engine has enjoyed a decade of iterative research from founder Keshav Pingali’s group at UT Austin and sets a new standard for performance as the world’s fastest graph processing engine.

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Single Machine

Katana Graph scales well on a single machine for the datasets that fit in memory.

Performance: Single Machine

Scale out

Katana Graph seamlessly scales out to a large number of machines in a distributed cluster.

Performance: Distributed Cluster

Next Generation Hardware (GPU)

Katana Graph supports single machine multi-GPUs as well as distributed GPU clusters.

Performance: Next Gen

Applications

Algorithms to analyze global properties or structural patterns of interest in graphs/networks where vertices represent entities of interest and edges represent relationships between these entities. These have applications in a wide range of domains such as chemical engineering, bioinformatics, and social sciences. Some of the applications include financial fraud detection, influencer analysis in social network communities, and optimizing supply distribution chains and logistics. 

Algorithms to find the shortest route between two vertices of a graph

  • Breadth-First Search
  • Single source shortest path
Analytics: Path Finding

Algorithms to identify vertices of importance a graph

  • PageRank 
  • Betweenness-Centrality
Analytics: Centrality

Algorithms to find densely connected communities in a graph

  • Connected Components
  • Louvain Clustering
  • Leiden Clustering
Analytics: Community Detection

Algorithms to discover graph structural properties

  • K-truss
  • K-core
Analytics: Properties

Algorithms for offline training of recommender system

  • Matrix-Completion
Analytics: Machine Learning

Count/list the number of triangles in a graph

Mining: Triangles

Count/list the complete (all vertices are connected to each other) subgraphs of size K

Mining: K-Cliques

Count/list the occurrences of a given subgraph in a graph

Mining: Subgraph

Count/list the number of occurrences of different subgraphs that are possible with K vertices in a graph

Mining: K-Motifs

Count/list all the frequently occurring subgraphs with fewer than K edges

Mining: K-FSM

Driving the next generation of graph computing

We believe in the power of data. At Katana Graph we are developing technologies to help people and businesses unleash the immense potential of their large scale irregular and unstructured data.

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