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.

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.

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.

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.

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.

Graph computing, which is much more than simply utilizing a graph database, and graph AI are capable of solving the most difficult data and analytics problems in the world. As seemingly ambitious a claim as this is, it’s relatively easy to make and significantly more difficult to prove.

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.

In this new era of Big Data, we can leverage the abundance of large Omics datasets: high-throughput biochemical assays that measure concentration and availability in cells such as genomics profile DNA, transcriptomics measure transcripts; proteomics and metabolomics quantify proteins and metabolites respectively.

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

ETL (extract, transform, and load) refers to the process data engineers use to pull data from diverse sources, transform it into a usable form for the desired process, and then load it into a system where data scientists and others can use it — for example, in predictive analytics. Much data analysis relies on ETL, but with the exponential increase in data volume and number of data sources, ETL is often a painful but necessary aspect of data science.

Graph mining is a growing field of study, and its use applies to many real-world applications. In particular, it has become increasingly crucial in transactional databases, where graphs are prevalent. Graph adoption has increased dramatically in major industries, starting with pharmaceutical, financial, security, and healthcare.

The PageRank algorithm uses incoming links between a graph’s nodes to rank the nodes, giving nodes with more incoming links a higher rank. For example, if the nodes represent web pages, the algorithm estimates the importance of each relative to the others by evaluating the number of links each page gets from other websites. Links from more important nodes count more for a node’s rank than ranks from less important nodes.

Swetha Polamreddy joined Katana Graph as Senior Product Marketing Manager in March of this year. She brings fifteen years of experience in information technology, semiconductors, advertising, education, and fintech. Having successfully collaborated with cross-functional teams in formulating strategies for interconnected projects, she specializes in turning complex technical offerings into outcome-based customer value propositions. Swetha holds an MBA, majoring in Marketing and Leadership Strategies from the Indian School of Business, Hyderabad, and holds a Master’s degree in Electrical Engineering from the University of Arkansas. We recently spoke with Swetha as part of our employee spotlight series.

Similarity metrics — quantification of how similar two entities are — exist at the core of important machine learning systems, including those aimed at recommendation systems, spam filtering, text mining and natural language processing (NLP), computer vision and facial recognition, clustering (for example, customer segment analysis), and in-pattern recognition in general. Since similarity is a subjective human concept, various interpretations of similarity exist and are selectively put to work for various kinds of machine learning tasks.

All recommendation systems require some sort of similarity metric, and the vast majority of recommendation systems employ machine learning techniques that involve similarity calculations. Items or people could be judged as similar by comparing attributes of entities that have been assigned by curators.

Keshav Pingali, CEO and co-founder of Katana Graph, was interviewed by JP Valentine on the March 30th AI in Action podcast, which explores the impact that Data Science, Machine Learning, and Artificial Intelligence are making on our everyday lives.

Intelligent graph computing approaches are at the fore of numerous mission-critical financial services use cases — and with good reason. With the surplus of data banks have at their disposal related to customers, market forces, and industry trends, relationship-savvy graph techniques are ideal for determining patterns in them that easily elude other approaches.

A Jaccard Coefficient is a measure between 0 and 1 (representing 0 to 100%) that quantifies the similarity between two sample sets. It compares members of each set to see which are shared and which are unique to each of the sets and the higher the coefficient, the more similar the two sets are deemed to be.

This article is the second in a series of short pieces introducing the Katana Graph Python Library for Graph Analytics. We will focus on single-source shortest path and connected components graph analytics algorithms in the library.

As product availability becomes vaster and more varied, consumers want simpler shopping experiences.

Graph processing is an emerging application area and a necessary tool for data scientists working with large datasets. Graphs in practical applications can grow to immense sizes. For example, social networks today can have billions of nodes and edges, so high-performance parallel computing is essential.

As Eric Schmidt, former CEO of Google, said in his 2017 book, How Google Works, "A.I. is imprecise, which means it can be unreliable as a partner. But, on the other hand, it's dynamic in the sense that it's changing all the time. It's emergent and does things that you don't expect. And, most importantly, it's capable of learning."

Few industries are as data-intensive — and as highly regulated — as life sciences. Organizations in this space routinely contend with data at an unparalleled scale compared to those in other verticals, especially for mission-critical use cases like precision medicine and bringing new pharmaceuticals to market.

Bold, innovative companies are leading the charge toward drug discovery through artificial intelligence and machine learning, resulting in a more effective, less expensive method for finding new drugs. However, the modern drug discovery process is loaded with Big Data, and it’s difficult to find small needles in immense haystacks.

The digital transformation of the financial services industry is one of the biggest things happening in the banking industry. The way people access and receive credit is quickly changing, and this change will affect all credit-focused lenders, including mortgage lenders.

Welcome to the April edition of Inside Katana Graph! We're here to give you a quick breakdown of the most important insights from our April blog posts.

Can high-performance computing and graph technology help to prevent economic disaster? Traditional analytics efforts have historically been afflicted by disconnected data, omissions and duplications, and incompatible data formats. These problems render most attempts at making data-based predictions ineffective at penetrating the data silos that obscure important relationships between the contributing causes of financial debacles.

Fraud and cyberattacks are projected to become more frequent and pernicious in the next year. Attackers use increasingly sophisticated technology and continually adapt and evolve their techniques to nab as much as possible before detection.

Organizations face increasingly sophisticated cybercrime threats and employ a spectrum of security tools to respond. One of these tools is the intrusion detection system (IDS). However, IDS solutions are often ineffective in preventing external cyberattacks due to their narrow scope and technical limitations.

In today’s world, data is so prevalent that we run the risk of drowning in it. How do you make sense of thousands of variables and data points?

The overarching goal of biomedical research is to develop therapeutics to cure diseases and improve human health. The Therapeutic Data Commons (TDC) provides datasets representing several aspects of drug discovery that are organized for processing by machine learning and AI. As reported on their website, by making the datasets available to the public, they are helping data and life scientists “translate algorithmic innovation into biomedical and clinical implementation.”

Cheminformatics is the merging of physical chemistry theory with computational techniques. The term can apply to industrial chemical research, environmental science, and pharmacology, but its primary domain is in the drug discovery process and related activities where chemical processes are integral to real-world life science solutions. One common use in drug discovery research is in the field of combinatorial chemistry, where thousands of chemical structures are generated simultaneously to be screened as drug candidates.

Bob DuCharme joined Katana Graph as a technical writer in November of last year. He is an accomplished software developer as well as a published technical and popular writer. He’s been involved with semantic web technologies since 2002 and has written books on SPARQL, XML, XSLT, and operating systems for O'Reilly, Manning Publications, and Prentice Hall. Bob has written nearly 100 online and print articles about IT and is proud of never having used the word “functionality” in any of them.

High-performance computing (HPC) technologies typically use multiple machines to enable computationally intensive tasks, such as AI, and render them more manageable.

As graphs become more available and easier to work with, enterprises are beginning to identify several common, consistent challenges as graph problems.

Identity Governance and Administration (IGA) is generally regarded as a set of best practices for data center operations. It covers hardware and software security, firewalls, vulnerability scanning, and patching. Although IGA has been increasingly recognized as a key security measure, the process faces the challenge of keeping up with the sheer volume of identities being managed in the cloud.

The need for security is exceptionally pressing in the financial services industry, which has been under an unrelenting barrage of cybercrime activities over the past few years.

In the fast-moving world of technology, it should never take weeks for analysts to sift through terabytes of data to gain insights and then act on those insights. The Katana Graph Intelligence Platform was conceived to help enterprises get answers in time to make a real difference.

The world has changed since our last Inside Katana Graph. We are living in unprecedented times. The pandemic crisis is ending but its effects on the global economy will be felt for years to come.

In Ukraine, everything is at stake. A country decimated by physical war, its people now live in the crosshairs of a new kind of conflict that could spell geopolitical disaster — cyberwar — which is often more about taking down a power grid (or exploding it) than about stealing your personal information. This is cyber espionage at its highest level. It's a new form of political assault with global implications: destructive programs targeting critical infrastructure and exfiltrating confidential information at governmental and societal levels. The burning question is what comes next?

The success of drug discovery innovation efforts depends heavily on the speed and precision with which hypotheses are generated and tested. Today’s challenges in health care include vast amounts of technical data used by researchers such as computational biologists and immunotherapists.

Justin Fine joined Katana Graph in September of 2021 bringing experience in big data gained at Microsoft, Neo4j, and Accenture. Justin’s background in applied math includes a lot of graph and matrix algebra for solving graph-like problems. He later worked in anti-money laundering and fraud analysis in government, banking, and telecommunications. Justin recently spoke with us as part of our employee spotlight series.

The term “analytics” encompasses an entire realm of thought about processing data. There are several different kinds of analytics contained within the overarching term, but all analytics strive to gain some sort of understanding from data. Modern analytical methods tend to be lumped into four groups: descriptive, diagnostic, predictive, and prescriptive.

The term “analytics” encompasses an entire realm of thought about processing data. There are several different kinds of analytics contained within the overarching term, but all analytics strive to gain some sort of understanding from data. Modern analytical methods tend to be lumped into four groups: descriptive, diagnostic, predictive, and prescriptive.

The term “analytics” encompasses an entire realm of thought about processing data. There are several different kinds of analytics contained within the overarching term, but all analytics strive to gain some sort of understanding from data. Modern analytical methods tend to be lumped into four groups: descriptive, diagnostic, predictive, and prescriptive.

Graph query is a cornerstone for the overarching graph intelligence concept, which also includes graph mining, graph analytics, and graph AI in a single platform with an unprecedented capacity for using data to swiftly obtain high-value business insights. Graph query is often the first phase in workloads that combine these graph computing domains.

The term “analytics” encompasses an entire realm of thought about processing data. There are several different kinds of analytics, but all of them strive to gain some sort of understanding from data. Modern analytical methods tend to be lumped into four groups: descriptive, diagnostic, predictive, and prescriptive.

Knowledge graphs integrate information about topics of interest and identify connections between them. The term knowledge graph first appeared in print in the 1970s, referring to modular components of academic courseware. Academic use of the term in the 1980s and 90s referred to the design of semantic networks. In 2007, graph-based knowledge repositories were developed for general-purpose information. In 2012, Google began using Knowledge Graph to describe a data model that would let them disambiguate concepts and retrieve relevant information about search targets and potentially related information.

The volume of biological data available to the pharmaceutical industry now threatens the capacity of conventional data analysis methods. The common cheminformatics task of generating drug hypotheses, for example, always involves complex pipelines operating on multiple platforms in multiple environments. This process often includes sifting through historical literature, public databases, and previous research findings.

The constant stream of data from web applications, mobile devices, and the Internet of Things is doubling every two years. Many organizations are drowning in customer data. Not only is the volume of data overwhelming, but the expansion of how organizations collect and consume that information is increasingly taxing on IT and Analytics departments.

February saw major developments in graph intelligence that moved the industry forward. This collection of posts from our blog was created to make it easier for you to keep up with the latest progress across the fields of knowledge graphs and graph-based AI. It features key insights and complementary information on health, fraud, security, and other topics concerning our community.

Graph mining is one of the four essential components of graph intelligence, a transformative concept for maximizing the enterprise value of graph data within a single platform. Each graph intelligence component (also including graph query, graph analytics, and graph AI) requires some functionality for recognizing patterns in graphs; graph mining and graph query rely most heavily on that functionality.

The number one thing that kills a startup organization is a lack of focus. There are many different opportunities available, and the first inclination is to try every one of them, but without proper focus managing the obligations becomes a hindrance.

A company's attitude toward product development is as vital as the company's attitude about growth. The Japanese business philosophy known as Kaizen encourages the creation of more innovative and engaging products. This is integral to the company's culture.

With cybersecurity becoming a crucial part of business operations, standard security solutions based on aging technology are inadequate to meet the challenges of today's cybercriminals. Existing infrastructures are increasingly vulnerable to attack. Manually examining correlations within mountains of data requires more security resources than most organizations can devote to the task, and the patterns security analysts must recognize to detect threats evolve constantly.

Fraud detection and prevention methods for electronic payment systems are overburdened by "false positives" or "false alerts." It is imperative to reduce these alarms when over 50% of a company’s business comes from existing customers.

Whether for modern Artificial Intelligence or traditional Business Intelligence, the insights derived from data for smart decision making and real-time action stem from analytics. Analyzing data to better achieve business objectives across domains is the main reason most organizations collect data to begin with.

Graph intelligence is a bold new movement encapsulating the best of graph technologies in today’s world of enterprise Artificial Intelligence (AI). It enables organizations to complete end-to-end workflows in a single platform, is fortified by a scalable graph computing engine, and eliminates unnecessary data movement.

Graph approaches are universally acknowledged for their ability to provide a contextualized understanding of the relationships between the diverse data they process. However, many options in this space are entirely dependent upon graph databases for their core functionality. Although such databases deliver undisputed enterprise utility, they’re only part of what true graph intelligence encompasses.

With financial services companies more exposed to risk than ever before, technologies designed to identify fraud faster are in high demand, and Katana Graph is up to the task.

To make your company stand apart from the competition and to remain profitable against market, financial, and supply chain risks, you need to be sure that you’re making the most of your data.

Keeping track of their health is a challenge for many people. Patients with multiple physicians across several unaffiliated medical groups require an efficient and effective way to track their health. In our fast-paced, modern life a growing number of hospitals are integrating clinical data from electronic medical and health records.

Our groundbreaking graph intelligence platform is changing the game in an increasingly data-driven competitive landscape, delivering a substantial edge in a growing number of industries and use cases. Read more about how a revolutionary graph engine brings to bear 10-100x faster performance and unmatched scalability on some of the biggest challenges in financial services, security, healthcare, and beyond.

Katana Graph is putting together a team that is passionate and devoted to creating products that will change the way businesses use data. This will foster innovation and creative solutions to enterprises by merging high-performance computing with the powerful insights of graph technology. A new era of predictive analytics and timely insights will put the massive data stores managed by enterprises to work for them in new and creative ways.

Information is elegant when it’s transformed into meaningful insights. Statistician Edward Tufte, a pioneer in simplifying complexity, teaches elegance of information design. Tufte has conceptualized visualization of data into multi-dimensional layers to better understand complex abstractions.

The Artificial Intelligence (AI) market is a highly competitive space, and AI's impact on the workplace is undeniable. This thriving market is paving the way for innovative new technologies that are poised to change the way we live, work, and play. As Gartner reported in November, “Worldwide artificial intelligence (AI) software revenue is forecast to total $62.5 billion in 2022, an increase of 21.3% from 2021.”

Roshan Dathathri leads the Graph Engine team at Katana Graph. His team is responsible for building the computation and communication engine that processes graphs efficiently on large clusters.

Katana Graph is off to a stellar start and we are looking forward to shaping how enterprises in domains ranging from financial services to health and life sciences to security glean insights from massive amounts of data.

The way we gather and organize knowledge is changing rapidly and artificial intelligence (AI) will have a far-reaching impact on our everyday lives, from healthcare to education.

Healthcare and life sciences continue to acquire vast amounts of data, but the real value of that data has been largely untapped.

The Language of Graph series distills the complexity and often mystifying terminology of knowledge graphs into business terms so that business leaders can understand the power and importance of the knowledge graph to business. The last three posts introduced Graph Query, Graph Analytics, and Graph Mining, and broke down some of their intricacies. In this post, we look at Graph Artificial Intelligence (Graph AI).

James Coffey joined Katana Graph in August 2021 as head of Developer Relations.

In today's increasingly hybrid work environment, companies must manage how they share and protect their information with more care than ever before. As the digitization of the workplace accelerates — into new spaces, such as mobile and cloud; using new technologies, like AI and machine learning; and to permit new access points, both on-premises and remote — businesses get to reap the rewards of enhanced innovation, insights and productivity. Yet rapidly forging into new frontiers leaves organizations more vulnerable than ever to cyber threats.

The more data a business needs to collect and use, the more knowledge graphs become necessary. But is there a business justification for the value an organization can expect from the knowledge graph? Enterprise companies need to assess the costs, risks, and return on investment (ROI) of the technology before purchasing.

The Language of Graph is a series of posts that attempts to tame some complexity, clarify graph terminology, and help business leaders understand the power that graph technology can bring to their business.

Pharmaceutical companies are facing soaring development costs and prolonged product development times that are driving the industry to search for Artificial Intelligence and graph tools to ease these pressures. These technologies have proven advantageous, allowing the industry to meet needs in the consumer environment more efficiently and streamline the drug discovery process.

Businesses generate around 7 septillion megabytes of data per day, but more than half of it is never monetized in any way (Splunk). Unseen connections in these data points could potentially identify what makes a company run smoothly, but finding a needle in a haystack is rarely the kind of time investment that businesses can make.

Happy Holidays and see you all in the new year!

John Rueter joined Katana Graph as head of Marketing in early 2021, bringing deep experience in strategic marketing, business analytics, graph technologies, and in building out high-performance marketing teams at several high-profile software companies. Throughout his career, John’s peers have applauded his leadership style, and his “roll up the sleeves” approach to building a successful business.

One hundred and ninety-four years after Beethoven's death, something unexpected occurred: an AI completed Beethoven’s 10th Symphony. It was performed in Beethoven’s birthplace of Bonn, Germany by the Beethoven Orchestra Bonn under the baton of Dirk Kaftan on October 9, 2021.

The Language of Graph is a series of posts that attempts to tame some complexity and demystify graph terminology.

Financial service businesses, especially banks, have longed for ways to better engage their customers with new products and services. Unfortunately, the limitations of the relational databases that house customer data provide only limited opportunities based on specific preset data schemas and rules. This can lead to good customers missing out on great opportunities from their bank.

Google introduced the term knowledge graph in 2012, referring to a general-purpose knowledge base that would not fit neatly into the tables of traditional databases.

Disruptive memory technologies are leading to significant improvements in both the capacity and the bandwidth of memories.

Asset misappropriation, bribery, false invoices and financial statement falsification remained steady during the pandemic. In fact, fraud has increased dramatically since the beginning of 2020.

Join us as we look back on some of your favorite Katana Graph blogposts of the last year – from platform primers to kaizen to founder interviews. As we flip our calendars to 2022, we look forward to further raising the bar on our content, so we can more effectively share our unique vision and latest innovations with you – our friends, customers, and partners.

Katana Graph’s CEO, Keshav Pingali, recently spoke with Jerome Knyszewski of Thrive Global. In the interview, Keshav explains that he strives to focus on “high-valued opportunities rather than getting distracted” and emphasizes quality over quantity regarding meeting customers’ needs.

“A lot of the time there is a window of opportunity within which, if your analytics completes, you can get insights and you can act on those insights. Then, you benefit from the analytics. But if the answer comes too late outside of that window of opportunity, then you might as well not have done the analytics.” - Keshav Pingali, CEO, Katana Graph.

To understand the threats and opportunities, and to be competitive in today’s enterprise world, businesses must gather structured and unstructured data from all available sources.

The founders of Katana Graph devised a technological solution to make massive amounts of data accessible and ease business anxiety.

One of the most fascinating concepts to emerge from Facebook was the social graph. Basically, social graphs mapped the user’s connections. The output was a graph defining and illustrating the relationships between the users, their connections, their connections’ connections, and more. Visualization of the graph along with its implications have been a great source of amusement.

Keynote highlights from CEO Keshav Pingali’s talk at the Knowledge Graph Conference 2021, “High-Performance Knowledge Graph Computing on the Katana Graph Platform.”

The Language of Graph series of posts will attempt to tame some complexity and clarify graph terminology. The first post in this series is focused on Graph Query.

The pandemic changed the way we all work. Work from home (WFH) has provided businesses with new challenges in Identity and Access Management (IAM) safety. Last year alone McAfee ATR observed an average of 588 threats per minute, an increase of 40% in the third quarter of 2020 (McAfee ATR, 2021).

Extortion is big business. If measured as a country, cybercrime would be the third largest economy after the U.S. and China (Morgan, 2020). It is an understatement to say cybercrime has increased considerably since 2013. Billionaire businessman and philanthropist Warren Buffet recently wrote that cybercrime is now mankind’s greatest challenge, suggesting that cyberattacks are a bigger threat than nuclear weapons.

The Pandora Papers findings were brought together by the International Consortium of Investigative Journalists (ICIJ). Pandora definitely earns its name, and we know all too well how the ancient Greek story of Pandora’s Box turned out. In a recent disclosure, twelve million documents revealed hidden wealth, tax avoidance and, in some cases, money laundering by some of the world's richest and most powerful. The files exposed how some of the most powerful people in the world - including more than 330 politicians from 90 countries - use secret offshore companies to hide their wealth, uncovering dirty deeds that make honest folk cringe.

Graph analytics products cover a wide range of topics and end users, so terms can often be used by one group of users but not by another. In the spirit of helping everyone understand graph analytics better, here are the top 15 terms worth knowing.

What is network graph visualization and how can you start to use it today? There are many different ways to display, render, and interact with your graph data. Analysts are using tools from desktop applications like Graphviz, Gephi, and Cytoscape, web-based libraries and visualization platforms like sigma.js and Linkurio.us or data science platforms such as Python and Jupyter notebooks. In this post we look at an example from each of these categories to help you understand the options you have available.

The reports of the increase in fraud, suspicious account activity, and money laundering have also increased spending on threat detection. Fraud impacts most industries, but financial institutions are often targets for the most advanced attacks. In financial institutions, these serious threats have historically been treated as IT problems, but are now seen as needing tighter integration with business analytics.

"The pandemic makes collaboration a little bit harder. There are definitely times when it would be great to have a whiteboard. But by and large, we've made a lot of progress and continue to have a lot of fun and largely be unaffected, so I think we're very lucky in that regard." - Chris Rossbach

Katana Graph is proud to announce: We’re Great Place to Work Certified! Ninety-eight percent of our employees at Katana Graph said this is a great place to work; for typical US companies the rate is fifty-nine percent. Ninety-six percent of our employees said they joined Katana Graph because they felt like they were warmly welcomed. Katana values the talent and energy of all its members.

Katana Graph’s unified intelligence platform is built from the ground up to drive innovation across a wide range of industries and use cases.

In part one of this topic, we looked at how network graph tools do more than just visualize data. How data is stored and represented is essential, as are the kinds of analysis available to different tools. In this post, we continue the discussion and describe the role that query languages, databases, and software licensing play in the network graph toolkit space.

One of the greatest obstacles to using data to solve problems is that 80% of the world’s data is unstructured. Businesses are struggling to use data to gain important and timely insights to solve pressing problems. But another equally paralyzing challenge is the congestion resulting from the sheer bulk of relevant business data. Katana Graph has a strategy and technology to help businesses with the daunting task of utilizing their data so that business can grow and produce.

The Katana Graph Intelligence Platform enables enterprises to accelerate data-driven business decisions from R&D to production with a flexible, scalable, easy-to-use platform. The Graph Intelligence Platform focuses on solving the three main pain points working with enterprise data: scale, performance, and ease of use.

Many people have heard about some of the more common - or at least most discussed in the early days - applications of graph technology, things like drug discovery and fraud detection. If I wanted to describe Katana Graph’s particular advantages to, say, a security knight interested in intrusion detection, where would I start?

The theoretical concept of networks is the basis of many modern approaches to analyzing data. At its most superficial level, network graph science represents a collection of concepts describing and analyzing entities and their relationships. A simple graph drawing is often used to introduce the idea of entities/nodes and their relationships/edges but is only the beginning. There are a variety of types of tools needed to have a complete network graph package.

A company’s attitude towards product development is as important as the company’s attitude about growth. There are plenty of technology companies that impose a process of perfection rather than one of iteration. The perfection mindset often creates a product having no path to innovation - that product is often one-and-done. A company’s approach to continuous improvement speaks to the company’s understanding of innovation.

By 2014 the Internet of Things (IoT) had entered the mainstream via smart home and elder care applications. Medical devices introduced at the annual Consumer Electronics Show made their way into the homes and the lives of consumers. Industrial and organizational applications followed. IoT consumer devices replaced single-function appliances like scales that provided some data but offered no real knowledge management. The adoption is so great that today estimates for active IoT devices range from 10 to 40 billion (Jovanović.2021).

At Katana Graph, we're building a graph computing platform and storage system that accelerates graph database, analytics, mining, and AI workloads and interoperates with industry-standard storage systems.

The Katana Graph Engine (KGE) is a scale-out platform for high-speed graph analytics, pattern mining and querying on heterogeneous clusters of CPUs and GPUs, providing unmatched compute capability for processing even the largest graphs such as web-crawl graphs with billions of vertices and trillions of edges. This document focuses on the use of KGE for ultrahigh- performance graph analytics.

Katana Graph, a high-speed graph analytics startup focused on processing large unstructured data sets, announced it has optimized its graph engine for the new 3rd generation Intel Xeon scalable processor and memory systems.

The increasing reliance on knowledge graphs parallels that of Artificial Intelligence for three irrefutable reasons.

Highlights from Jelani Harper's insideBIGDATA article 'Knowledge Graphs 2.0: High Performance Computing Emerges' featuring Keshav Pingali, CEO of Katana Graph.

Coronavirus created an unprecedented, worldwide health crisis, killing millions and causing widespread economic and social disruption.

Who says graph intelligence isn’t a creative pursuit? In fact, graph and high-performance computing can sometimes come together to inspire some fascinating, one-of-a-kind AI-driven art. Take two really interesting projects from different eras that incorporate knowledge graph.