What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-26
Have you ever wondered how routing apps like Google Maps find the best route from one place to another? Finding that route is solved by the Shortest Path graph algorithm. Today, graph algorithms are moving from the classroom to a host of important and valuable operational and analytical applications. This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...TigerGraph
Graph-based investigation often enables us to identify individuals who are of special interest, and their uniqueness is due in part to their pattern of interactions. For example:
-A patient whose carepath journey leverages best-practices gained from using pattern matching algorithms that find similar issues among the data of 50 million patients
-An individual who builds a successful portfolio by implementing actions recommended by similarity algorithms that find equivalent actions by successful investors
-A participant in a criminal ring whose attempts at swindling are blocked by matching them to patterns of known fraudulent activity
Once you have identified such a pattern and a key individual, you want to search your data for similar occurrences. Similarity algorithms are the answer.
Using Graph Algorithms for Advanced Analytics - Part 5 ClassificationTigerGraph
What atmospheric data will help you predict if it's going to rain, snow, or be windy? What position should that new athlete play? How well can you guess a person's demographic background, based on their chat activity? These are all classification problems -- trying to pick the right category or label for an entity, based on observable features. They can also be solved with machine learning.
Join us for this 30-minute webinar to hear from Zach Blumenfeld, Neo4j’s Data Science Specialist, to learn the basics of Graph Neural Networks (GNNs) and how they can help you to improve predictions in your data.
Graph Databases and Machine Learning | November 2018TigerGraph
Graph Database and Machine Learning: Finding a Happy Marriage. Graph Databases and Machine Learning
both represent powerful tools for getting more value from data, learn how they can form a harmonious marriage to up-level machine learning.
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-26
Have you ever wondered how routing apps like Google Maps find the best route from one place to another? Finding that route is solved by the Shortest Path graph algorithm. Today, graph algorithms are moving from the classroom to a host of important and valuable operational and analytical applications. This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...TigerGraph
Graph-based investigation often enables us to identify individuals who are of special interest, and their uniqueness is due in part to their pattern of interactions. For example:
-A patient whose carepath journey leverages best-practices gained from using pattern matching algorithms that find similar issues among the data of 50 million patients
-An individual who builds a successful portfolio by implementing actions recommended by similarity algorithms that find equivalent actions by successful investors
-A participant in a criminal ring whose attempts at swindling are blocked by matching them to patterns of known fraudulent activity
Once you have identified such a pattern and a key individual, you want to search your data for similar occurrences. Similarity algorithms are the answer.
Using Graph Algorithms for Advanced Analytics - Part 5 ClassificationTigerGraph
What atmospheric data will help you predict if it's going to rain, snow, or be windy? What position should that new athlete play? How well can you guess a person's demographic background, based on their chat activity? These are all classification problems -- trying to pick the right category or label for an entity, based on observable features. They can also be solved with machine learning.
Join us for this 30-minute webinar to hear from Zach Blumenfeld, Neo4j’s Data Science Specialist, to learn the basics of Graph Neural Networks (GNNs) and how they can help you to improve predictions in your data.
Graph Databases and Machine Learning | November 2018TigerGraph
Graph Database and Machine Learning: Finding a Happy Marriage. Graph Databases and Machine Learning
both represent powerful tools for getting more value from data, learn how they can form a harmonious marriage to up-level machine learning.
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
Comparing three data ingestion approaches where Apache Kafka integrates with ...HostedbyConfluent
Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customers’ data architecture. We have seen the integration in three different layers around TigerGraph’s data flow architecture, and many key use case areas such as customer 360, entity resolution, fraud detection, machine learning, and recommendation engine. Firstly, TigerGraph’s internal data ingestion architecture relies on Kafka as an internal component. Secondly, TigerGraph has a builtin Kafka Loader, which can connect directly with an external Kafka cluster for data streaming. Thirdly, users can use an external Kafka cluster to connect other cloud data sources to TigerGraph cloud database solutions through the built-in Kafka Loader feature. In this session, we will present the high-level architecture in three different approaches and demo the data streaming process.
A VERY high level over view of Graph Analytics concepts and techniques, including structural analytics, Connectivity Analytics, Community Analytics, Path Analytics, as well as Pattern Matching
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
Easily Identify Sources of Supply Chain GridlockNeo4j
Join us for this 20-minute webinar to hear from Nick Johnson, Product Marketing Manager for Graph Data Science, as he explains the fundamentals of Neo4j Graph Data Science and its applications in optimizing supply chain management. Discover how leveraging graph analytics can help you identify bottlenecks, reduce costs, and streamline your supply chain operations more efficiently.
Government GraphSummit: Leveraging Graphs for AI and MLNeo4j
Phani Dathar, Ph.D., Data Science Solution Architect, Neo4j
Relationships are highly predictive of behavior. Graph technology abstracts connections in our data so businesses can apply relationships and network structures to make better predictions. Hear about the journey from graph analytics and machine learning to graph-enhanced AI. We’ll also cover how enterprises are using graph data science in areas such as fraud, targeted marketing, healthcare, and recommendations.
Graph Gurus Episode 32: Using Graph Algorithms for Advanced Analytics Part 5TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-32
By watching this webinar you will:
-See how similarity algorithms are used to calculate "distances" between entities.
-Learn what data scientists mean when they say Labels and Training.
-Understand the full workflow for the k-Nearest Neighbor machine learning technique, from computing distances to predicting labels for a given value of k, to learning the best value of k.
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
Comparing three data ingestion approaches where Apache Kafka integrates with ...HostedbyConfluent
Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customers’ data architecture. We have seen the integration in three different layers around TigerGraph’s data flow architecture, and many key use case areas such as customer 360, entity resolution, fraud detection, machine learning, and recommendation engine. Firstly, TigerGraph’s internal data ingestion architecture relies on Kafka as an internal component. Secondly, TigerGraph has a builtin Kafka Loader, which can connect directly with an external Kafka cluster for data streaming. Thirdly, users can use an external Kafka cluster to connect other cloud data sources to TigerGraph cloud database solutions through the built-in Kafka Loader feature. In this session, we will present the high-level architecture in three different approaches and demo the data streaming process.
A VERY high level over view of Graph Analytics concepts and techniques, including structural analytics, Connectivity Analytics, Community Analytics, Path Analytics, as well as Pattern Matching
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
Easily Identify Sources of Supply Chain GridlockNeo4j
Join us for this 20-minute webinar to hear from Nick Johnson, Product Marketing Manager for Graph Data Science, as he explains the fundamentals of Neo4j Graph Data Science and its applications in optimizing supply chain management. Discover how leveraging graph analytics can help you identify bottlenecks, reduce costs, and streamline your supply chain operations more efficiently.
Government GraphSummit: Leveraging Graphs for AI and MLNeo4j
Phani Dathar, Ph.D., Data Science Solution Architect, Neo4j
Relationships are highly predictive of behavior. Graph technology abstracts connections in our data so businesses can apply relationships and network structures to make better predictions. Hear about the journey from graph analytics and machine learning to graph-enhanced AI. We’ll also cover how enterprises are using graph data science in areas such as fraud, targeted marketing, healthcare, and recommendations.
Graph Gurus Episode 32: Using Graph Algorithms for Advanced Analytics Part 5TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-32
By watching this webinar you will:
-See how similarity algorithms are used to calculate "distances" between entities.
-Learn what data scientists mean when they say Labels and Training.
-Understand the full workflow for the k-Nearest Neighbor machine learning technique, from computing distances to predicting labels for a given value of k, to learning the best value of k.
Graph Gurus Episode 29: Using Graph Algorithms for Advanced Analytics Part 3TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-29
In this webinar you will:
-Hear about use cases for community detection graph algorithms
-Learn how to select the right algorithm for your use case
-Be able to run and tailor GSQL graph algorithms
Full Webinar: https://info.tigergraph.com/graph-gurus-28
In this webinar, we will use the recommendation system problem, which can be efficiently solved as a graph problem, to demonstrate the in-database training capability of TigerGraph, a native graph database. A hybrid (memory-based + model-based) recommendation system will be implemented in TigerGraph. Specifically, the latent factor model used for recommendation will be trained within the database.
In this Graph Gurus episode, we will:
-Review multiple widely-used recommendation methods
-Introduce the concept of in-database machine learning
-Present an in-database machine learning solution for a real time recommendation system
A Fast Intro to Fast Query with ClickHouse, by Robert HodgesAltinity Ltd
Slides for the Webinar, presented on March 6, 2019
For the webinar video visit https://www.altinity.com/
Extracting business insight from massive pools of machine-generated data is the central analytic problem of the digital era. ClickHouse data warehouse addresses it with sub-second SQL query response on petabyte-scale data sets. In this talk we'll discuss the features that make ClickHouse increasingly popular, show you how to install it, and teach you enough about how ClickHouse works so you can try it out on real problems of your own. We'll have cool demos (of course) and gladly answer your questions at the end.
Speaker Bio:
Robert Hodges is CEO of Altinity, which offers enterprise support for ClickHouse. He has over three decades of experience in data management spanning 20 different DBMS types. ClickHouse is his current favorite. ;)
Shift Remote: AI: Smarter AI with analytical graph databases - Victor Lee (Ti...Shift Conference
Today's analytical graph databases are taking organizations to another level by connecting all their data, representing knowledge better, and obtaining answers to deeper questions in real time. These benefits extend to the world of machine learning and AI. This talk will illustrate several ways in which graph databases and graph analytics can deliver smarter AI:
1. Unsupervised learning with graph algorithms.
2. Feature extraction and enrichment with graph patterns.
3. In-database ML techniques for graphs
Scaling up business value with real-time operational graph analyticsConnected Data World
Graph-based solutions have been in the market for over a decade with deployments in financial services, healthcare, retail, and manufacturing. The graph technology of the past limited them to simple queries (1 or 2 hops), modest data sizes, or slow response times, which limited their value.
A new generation of fast, scalable graph databases, led by TigerGraph, is opening up a new world of business insight and performance. Join us, as we explore some new exciting use cases powered by native parallel graph database with storage and computation capability for each node:
A large financial services payment provider is using graph-based pattern detection (7 to 11 hop queries) to detect more fraud and money laundering in real time, handling peak volume of 256,000 transactions per second.
IceKredit, an innovative FinTech is transforming the near-prime and sub-prime credit market in United States, China and South Asian countries with customer 360 analytics for credit approval and ongoing monitoring.
A biotech and pharmaceutical giant is building a prescriber and patient 360 graph and using multi-hop exploratory and analytic queries to understand the most efficient ways of launching a new drug for maximum return.
Wish.com is delivering real-time personalized recommendations to increase eCommerce revenue.
Graph Gurus Episode 37: Modeling for Kaggle COVID-19 DatasetTigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-37
In this Graph Gurus Episode, we:
-Learn how to process text and extract entities (words and phrases) as well as classes linking the entities using SciSpacy, a Natural Language Processing (NLP) tool.
-Import the output of NLP and semantically link it in TigerGraph
-Run advanced analytics queries with TigerGraph to analyze the relationships and deliver insights
Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDESSubhajit Sahu
For the IPDPS ParSocial event a presentation submission is required by 15th May. The event is on 3rd June.
https://gist.github.com/wolfram77/51b15ca09eb28f6909673a2deb1a314d
DYNAMIC BATCH PARALLEL
ALGORITHMS FOR UPDATING
PAGERANK
Subhajit Sahut, Kishore Kothapallit and Dip Sankar Banerjeet
tInternational Institute of Information Technology Hyderabad, India.
tIndian Institute of Technology Jodhpur, India.
subhajit.sahu@research. ,kkishore@iiit.ac.in, dipsankarb@iitj.ac.in
This work is partially supported by a grant from the Department of Science and Technology (DST), India, under the
National Supercomputing Mission (NSM) R&D in Exascale initiative vide Ref. No: DST/NSM/R&D Exascale/2021/16.
FACEBOOK 15 TAKING A PAGE OUT
OF GOOGLE’S PLAYBOOK 10 STOP
FAKE NEWS FROM GOING VIRAL
PUBLISHED APR 2015 BY SALVADOR RODRIGUEZ
Click-Gap: When is Facebook
is driving disproportionate
amounts of traffic to
websites.
Effort to rid fakes news
from Facebook’s services.
Is a website relying on
Facebook to drive
significant traffic, but not
well ranked by the rest of
the web?
Also News Citation Graph.
PAGERANK APPLICATIONS
Ranking of websites.
Measuring scientific impact of researchers.
Finding the best teams and athletes.
Ranking companies by talent concentration.
Predicting road/foot traffic in urban spaces.
Analysing protein networks.
Finding the most authoritative news sources
Identifying parts of brain that change jointly.
Toxic waste management.
PAGERANK APPLICATIONS
Debugging complex software systems (Moni torRank)
Finding the most original writers (BookRank)
Finding topical authorities (TwitterRank)
WHAT IS PAGERANK
l—-d
Plu = Cus + ——
UCIiNny
Pru
u->v = (1-—d) x
“us ( ) outdegy,
PageRank is a lLink-analysis algorithm.
By Larry Page and Sergey Brin in 1996.
For ordering information on the web.
Represented with a random-surfer model.
Rank of a page is defined recursively.
Calculate iteratively with power-iteration.
InfluxDB 101 – Concepts and Architecture by Michael DeSa, Software Engineer |...InfluxData
Complete introduction to time series, the components of InfluxDB, how to get started, and how to think of your metrics problems with the InfluxDB platform in mind. What is a tag, and what is a value? Come and find out!
DataStax | Network Analysis Adventure with DSE Graph, DataStax Studio, and Ti...DataStax
Ride along as we use network analysis techniques to derive insights from our graph. We will begin by using exploratory analysis techniques to develop a high level understanding of our data. After gaining familiarity in the aggregate, we will select key elements of the graph for detailed inspection and graph visualization.
We will explore fundamental techniques that bridge the gap between academic network analysis concepts and pragmatic problem solving approaches for real-world property graphs at scale.
Prior network analysis expertise is not required. Source code and reproducibles will be made publicly available. Please try this at home.
About the Speaker
Bob Briody Software Engineer, DataStax
Bob is a diverse developer with over 10 years of experience across the stack. He joined DataStax as part of the Aurelius acquisition in 2015. Since then he has contributed to the design and development of DataStax Studio, with a focus on graph interaction and visualization. Bob is also a contributor to the Apache TinkerPop project.
Data Science at Scale on MPP databases - Use Cases & Open Source ToolsEsther Vasiete
Pivotal workshop slide deck for Structure Data 2016 held in San Francisco.
Abstract:
Learn how data scientists at Pivotal build machine learning models at massive scale on open source MPP databases like Greenplum and HAWQ (under Apache incubation) using in-database machine learning libraries like MADlib (under Apache incubation) and procedural languages like PL/Python and PL/R to take full advantage of the rich set of libraries in the open source community. This workshop will walk you through use cases in text analytics and image processing on MPP.
DevOpsDays Phoenix 2018: Using Prometheus and Grafana for Effective Service D...r4j4h
Presented at DevOpsDays Phoenix 2018, in this talk I demonstrate what a potential end-state developer-oriented Service Dashboard can look like and discuss what it took to get there. I discuss some of the trade-offs involved, such as the merits between which system to utilize for Alerts, and go over some ways to integrate lesser-known features to make dashboard users and alert responders have an easier time getting to what they need to.