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  • Blog
    Optimizing Large-Scale Distributed Graph Neural Networks on Intel CPUs
    Optimizing Large-Scale Distributed Graph Neural Networks on Intel CPUs

    Training Graph Neural Networks on Large Graphs Graph neural networks (GNNs) are a powerful tool for applying machine learning techniques to graph data. They allow us to generate or transform features on vertices and edges using deep neural networks to make them useful for a wide range of applications including fraud detection, product recommendation, and drug discovery. 

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  • Blog
    Rethinking Buyer Behavior Algorithms
    Rethinking Buyer Behavior Algorithms

    To standard traffic analyzers, one click is as good as another. Our impulse purchases and our most prized procurements are all weighted equally by the algorithms through which vendors characterize us as consumers. We still see advertisements for now-shelved quarantine hobbies as we scroll through our daily news because click-counting and similar metrics have failed to reliably predict our actual interest levels.

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  • Blog
    Graph Neural Networks for Credit Modeling
    Graph Neural Networks for Credit Modeling

    The financial services sector has many early adopters of sophisticated analytics techniques involving graph computing. Graph AI is regularly used in this industry for applications such as fraud detection, anti-money laundering, and other criminal activities.

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  • Blog
    Katana Graph’s Analytics Python Library
    Katana Graph’s Analytics Python Library

    As businesses grow and face increasing data challenges, they must find ways to tackle more expansive problems in shorter time windows. The most essential tools a company has at its disposal for addressing real-world data problems are modern algorithms. Businesses that take an algorithmic approach can tackle bigger problems with larger numbers of variables and can make better decisions than those that don't. Data is unquestionably the world’s most valuable resource today.

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  • Blog
    K-Core and K-Truss Algorithms
    K-Core and K-Truss Algorithms

    K-core and k-truss algorithms assist with community search in large graphs and are used to identify cohesive portions of a graph based on specific metrics, particularly in mining applications. Discovering dense subgraphs is an essential task in graph mining, and is valuable in the analysis of social networks, biology, and identifying crime rings. Both algorithms proceed by iteratively removing components that do not meet a specified metric to produce a subgraph. By removing extraneous components, a user of the algorithm can produce simplified versions of large, complex graphs to provide clarity and understandability. Cohesive subgraphs allow the user to identify significant connections within their graph without a great deal of computational requirements.

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  • Blog
    Clustering Coefficient Functions
    Clustering Coefficient Functions

    The clustering coefficient algorithm is typically used on homogeneous, undirected graphs to determine which nodes cluster together and the likelihood that a node’s neighbors are also connected. For example, if a person’s friends are also friends with each other, the node representing that person has a high clustering coefficient. A low clustering coefficient, on the other hand, indicates a graph is composed of several weak ties. A clustering coefficient might be viewed roughly as the ratio of common friends in a social network compared to all possible connections a person might have.

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  • Blog
    Community Detection Using Label Propagation Algorithm
    Community Detection Using Label Propagation Algorithm

    Many networks — of people, bacteria, or computers — have a community-like structure. Quickly finding communities within large networks is a common task of the label propagation algorithm (LPA), a semi-supervised machine learning algorithm that assigns labels to the vertices of a graph that represents such a network.

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  • Blog
    Louvain Community Detection
    Louvain Community Detection

    Louvain clustering is an algorithm for community detection that serves as an unsupervised, agglomerative, bottom-up clustering method for undirected graphs.

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Data Sheets

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  • Data Sheets
    Credit Modeling
    Financial Services Credit Modeling

    Achieving new breakthrough levels of improved lending outcomes requires continuous improvement of credit modeling using the most advanced predictive technologies possible, particularly graph AI.

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  • Data Sheets
    360 Degree Customer Insights
    Financial Services 360 Degree Customer Insights

    Organizations have more customer data sources than ever—purchase history, campaign activity, interactions across all channels, demographics and countless more—which together can reveal new, 360-degree customer insights, including predicting future needs and wants.

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  • Data Sheets
    RDKit Integration
    Health & Life Sciences RDKit Integration

    The success of your drug discovery innovation efforts depends heavily on the speed and accuracy in which hypotheses can be generated and tested. The Katana Graph intelligence platform fulfills this essential need for life sciences companies in ways alternative solutions simply cannot match.

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  • Data Sheets
    Intelligent Identity Governance & Administration (IGA)

    The 2020 global pandemic forced organizations to accommodate a sudden, massive spike in remote work, business and communications. Many companies have found their identity and access management (IAM) systems cannot keep up with today’s highly complex and demanding identity landscape.

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  • Data Sheets
    The Katana Graph Intelligence Platform

    Katana Graph provides a Graph Intelligence Platform offering elastic performance at massive scale for Graph Queries (contextual search), Graph Analytics (path finding, centrality and community detection), Graph Mining (pattern discovery), and Graph AI (prediction).

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  • Data Sheets
    System Intrusion Detection

    Katana Graph Intelligence Platform detects a broad spectrum of intrusions in real time that might otherwise evade traditional IDS tools, through the power of next-generation graph analytics, mining and AI.

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  • Data Sheets
    Industry Overview: Life Sciences
    Health & Life Sciences Industry Overview: Life Sciences

    Katana Graph offers a breakthrough graph intelligence platform that enables life sciences organizations to reap the huge potential of graph processing and AI, at levels of scale and performance no other data platform can match.

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  • Data Sheets
    Drug Discovery
    Health & Life Sciences Drug Discovery

    Katana Graph enables life sciences knowledge workers to store, query, mine and develop AI models using heterogeneous data sources, to reveal breakthrough insights at levels of scale and performance no other data platform can match.

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  • Data Sheets
    Fraud Detection
    Financial Services Fraud Detection

    Katana Graph has developed a next generation graph intelligence platform that provides breakthrough processing essential for today’s real-time anti-fraud applications. Katana Graph efficiently handles highly complex graph queries, algorithms and deep learning models, at massive scale and speed that other graph solutions simply cannot match.

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  • Data Sheets
    Industry Overview: Security

    Organizations are actively improving customer engagement and optimizing business operations. New technologies to fulfill these demands have been introduced, from the Internet of Things (IoT) to SaaS solutions capable of managing the most complex networks of customers, partners and distributors. However, these technologies have also become targets for cybercrime.

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  • Data Sheets
    Industry Overview: Financial Services

    Katana Graph helps financial services companies more effectively detect fraud in real time, while also seizing new business opportunities through expanded, personalized services.

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