Graph analytics - PowerPoint PPT Presentation


Efficient Fraud Management with Data Analytics

Learn the importance of data analytics in fraud management and how it can streamline risk assessment, prevention, detection, audit planning, and investigation processes. Discover key areas where data analytics can make a difference and avoid common mistakes in your fraud analytics plan. Embrace data

2 views • 33 slides


Demystifying Data Analytics: Your Guide to Effective

\"Fixity EDX offers top-notch upskilling opportunities for students and professionals with data analyst, skill development, and corporate training programs. Gain high-quality skills and industry-recognized certification for enhanced career prospects.\" \n\nAre you intrigued by the vast potential of

1 views • 2 slides



Graph Machine Learning Overview: Traditional ML to Graph Neural Networks

Explore the evolution of Machine Learning in Graphs, from traditional ML tasks to advanced Graph Neural Networks (GNNs). Discover key concepts like feature engineering, tools like PyG, and types of ML tasks in graphs. Uncover insights into node-level, graph-level, and community-level predictions, an

3 views • 87 slides


Exploring Data Analytics: Introduction, Terminology, Challenges, Platforms, Tools, Applications

Delve into the world of data analytics through this comprehensive guide covering topics such as the definition of data, big data, analytics vs analysis, the importance of data analytics, real-world applications, and more. Explore the classification of data, the 3Vs of big data, and how data analytic

4 views • 39 slides


Harnessing Climate Data Analytics for Sustainable Supply Chain

In the end Vinz Global's dedication to using climate data analytics to build sustainable supply chains illustrates its ability to lead positive change and generating benefits for society and the environment. Through integrating climate data analytics into its business operations, Vinz Global gains i

10 views • 4 slides


Localised Adaptive Spatial-Temporal Graph Neural Network

This paper introduces the Localised Adaptive Spatial-Temporal Graph Neural Network model, focusing on the importance of spatial-temporal data modeling in graph structures. The challenges of balancing spatial and temporal dependencies for accurate inference are addressed, along with the use of distri

3 views • 19 slides


Graph Neural Networks

Graph Neural Networks (GNNs) are a versatile form of neural networks that encompass various network architectures like NNs, CNNs, and RNNs, as well as unsupervised learning models such as RBM and DBNs. They find applications in diverse fields such as object detection, machine translation, and drug d

2 views • 48 slides


Understanding Neo4j Graph Database Fundamentals

This comprehensive presentation delves into the fundamentals of Neo4j graph database, covering topics such as the definition of graph databases, reasons for their usage, insights into Neo4j and Cypher, practical applications like data flow analysis, and hands-on instructions on creating and querying

0 views • 20 slides


Meticulous Research® Releases In-Depth Report on Global Cloud Analytics Market Forecast

Cloud Analytics Market Size, Share, Forecast, & Trends Analysis by Offering (Solutions, Services), Type (Predictive Analytics, Diagnostic Analytics, Prescriptive Analytics), Deployment Mode, Sector (BFSI, Retail & E-commerce, Healthcare & Life Sciences), and Geography - Global Forecast to 2031\n

0 views • 4 slides


Your Current Business Analytics Tool Is No Longer Enough_ What’s Next for Data-Driven Decisions_

Discover why your current business analytics tool may no longer meet the demands of today's data-driven landscape. This blog explores the limitations of outdated analytics platforms and guides you through the essential features of next-generation tools that can enhance your decision-making capabilit

2 views • 7 slides


Exploring Graph-Based Data Science: Opportunities, Challenges, and Techniques

Graph-based data science offers a powerful approach to analyzing data by leveraging graph structures. This involves using graph representation, analysis algorithms, ML/AI techniques, kernels, embeddings, and neural networks. Real-world examples show the utility of data graphs in various domains like

3 views • 37 slides


Developing a Teaching Portfolio for Online Doctoral Workshop on Supply Chain Analytics

In this workshop, distinguished panelists including Ananth Iyer, Apurva Jain, Subodha Kumar, and Yao Zhao share insights and expertise on supply chain analytics. Topics include program introductions, audience engagement, format, content criteria, and analytics applications. Participants will gain va

1 views • 7 slides


Impact of Data Analytics and Consulting Activities on Internal Audit Quality

This research examines how the use of data analytics and consulting activities affect perceived internal audit quality. The study investigates the relationship between these factors and top management's perception of internal audit quality. Through online scenario-based experiments with middle and t

2 views • 11 slides


Is Your Analytics Software Lying to You_ How to Spot and Correct Data Bias

Data bias can distort your analytics and lead to misguided decisions. In this blog, learn how to identify common signs of data bias, understand its impacts, and explore effective strategies to correct it. Enhance the accuracy and reliability of your insights with practical tips and advanced tools, e

3 views • 8 slides


Exploring Deep Graph Theory: Philosophical Implications and Misconceptions

Delve into the realm of Deep Graph Theory where graph theory statements are analyzed beyond their conventional scope to uncover philosophical insights and correct misunderstandings. Discover the essence of trees, forests, and the unique relationship where every tree is regarded as a forest. Addition

0 views • 13 slides


Unleashing the Power of Business Analytics for Enhanced Decision-Making

Businesses are leveraging data and analytics capabilities to transform decision-making processes. This shift has been driven by the availability of vast amounts of data, improved computational power, and sophisticated algorithms. The incorporation of business analytics in various sectors like market

0 views • 9 slides


Understanding Graph Theory Fundamentals

Delve into the basics of graph theory with topics like graph embeddings, graph plotting, Kuratowski's theorem, planar graphs, Euler characteristic, trees, and more. Explore the principles behind graphs, their properties, and key theorems that define their structure and connectivity.

0 views • 17 slides


Association Rules with Graph Patterns: Exploring Relationships in Data

Dive into the world of association rules with graph patterns, where relationships and connections are analyzed through nodes and edges. Discover how to define association rules, identify customers, and uncover interesting patterns using graph-based techniques. Explore traditional and graph-pattern a

2 views • 18 slides


Solving the Professors to Coffee Lounge Problem: A Graph Theory Approach

An intriguing mathematical problem is presented where new faculty members at TIMS must be assigned to coffee lounge alcoves in a way that ensures no two new members meet after the first day. By constructing a graph based on meet-up timings, analyzing clashes, and determining intervals, this scenario

1 views • 19 slides


Exploring the Impact of Randomness on Planted 3-Coloring Models

In this study by Uriel Feige and Roee David from the Weizmann Institute, the effect of randomness on planted 3-coloring models is investigated. The research delves into the NP-hard nature of 3-coloring problems, introducing a hosted coloring framework that involves choices like the host graph and th

0 views • 55 slides


Leveraging Predictive Analytics in Mobile App Development_ Enhancing User Experience and Retention

Discover how predictive analytics is transforming the mobile app development landscape in our latest blog, How Predictive Analytics is Shaping the Future of Mobile App Development. By leveraging data and machine learning models, predictive analytics

0 views • 4 slides


Managing Large Graphs on Multi-Cores with Graph Awareness

This research discusses the challenges in managing large graphs on multi-core systems and introduces Grace, an in-memory graph management and processing system with optimizations for graph-specific and multi-core-specific operations. The system keeps the entire graph in memory in smaller parts and p

0 views • 14 slides


Maria's Bike Journey Graph Analysis

Maria's bike journey graph depicts her distance from home as she rode to meet friends and run errands before returning home. The graph shows her stops for errands, changes in direction, and her path back home. By interpreting the key features of the graph, such as intercepts and intervals, we can an

0 views • 15 slides


Balanced Graph Edge Partition and Its Practical Applications

Balanced graph edge partitioning is a crucial problem in graph computation, machine learning, and graph databases. It involves partitioning a graph's vertices or edges into balanced components while minimizing cut costs. This process is essential for various real-world applications such as iterative

0 views • 17 slides


Understanding Spanning Trees and Minimum Spanning Trees

Explore the concept of spanning trees and minimum spanning trees in graph theory through an in-depth lecture outline covering topics like Cut Property, Cycle Property, Kruskal's Algorithm, and more. Delve into the significance of Minimum Spanning Trees (MSTs) as the lowest-cost spanning tree of a gr

0 views • 41 slides


Graph Pattern Matching Challenges and Solutions

Graph pattern matching in social networks presents challenges such as costly queries, excessive results, and query focus issues. The complexity of top-k and diversified pattern matching problems requires heuristic algorithms for efficient solutions. Finding best candidates for project roles involves

0 views • 19 slides


Understanding Small Set Expansion in Johnson Graphs

In this detailed piece, Subhash Khot, Dor Minzer, Dana Moshkovitz, and Muli Safra explore the fascinating concept of Small Set Expansion in Johnson Graphs. The Johnson Graph is defined as a representation where nodes are sets of size K in a universe of size N, and two sets are connected if they inte

0 views • 14 slides


Understanding Analytics for Target (A4T) Integration

Analytics for Target (A4T) is a powerful cross-solution integration that enables you to create target activities based on Analytics conversion metrics and audience segments. This integration utilizes Analytics reports for result examination and drives optimization program analysis. A4T provides valu

0 views • 33 slides


Vertex-Centric Programming for Graph Neural Networks

Seastar presents a vertex-centric programming approach for Graph Neural Networks, showcasing better performance in graph analytic tasks compared to traditional methods. The research introduces the SEAStar computation pattern and discusses GNN programming abstractions, execution, and limitations. Dee

0 views • 17 slides


Business Analytics Program at Wake Tech Community College

Wake Tech Community College offers an Associate in Applied Science degree program in Business Analytics. The program aims to prepare students for careers in analytics fields such as Business Intelligence, Marketing Analytics, Finance Analytics, and Logistics Analytics. With a focus on employability,

0 views • 13 slides


Graph Property Testing and Algorithms Overview

Explore testable bounded degree graph properties, sparse graphs, d-bounded degree graphs, hyperfinite graphs, arboricity, maximum matching algorithms, and sublinear time approximation algorithms in graph data streams. Learn about various graph models and properties with examples, showcasing the impo

0 views • 53 slides


Understanding Graph Modeling and DFS Applications

Explore the world of graph modeling and DFS applications through lectures on graph vocabulary, edge classification in directed graphs, and the use of DFS to find cycles. Discover the significance of tree edges, back edges, forward edges, and cross edges in graph traversal. Learn how DFS can be utili

0 views • 32 slides


Graph-Based Knowledge Representation in Modelling: A Comprehensive Overview

This content delves into graph-based knowledge representation in modelling, detailing concepts such as recipe-ingredient relationships, formalisms for generalizing graph representation, and conceptual graphs by John F. Sowa. It explores how different interpretations describe the association between

0 views • 19 slides


Understanding Graph Databases and Neo4j

Graph databases offer a flexible way to manage data by representing relationships between nodes. Neo4j is a popular graph database system that uses Cypher for querying. This guide provides insights into graph database concepts, advantages, and getting started with Neo4j, including creating nodes and

0 views • 39 slides


Solving Train Track Problems Using Interval Graphs and Graph Coloring

Presented by Manvitha Nellore, this content addresses real-world train track problems in busy cities by proposing solutions through interval graphs and graph theory. The approach involves allotting tracks to trains by scheduling with time intervals to avoid conflicts. An interval graph is defined, a

0 views • 15 slides


Introduction to Google's Pregel Distributed Analytics Framework

Google's Pregel is a large-scale graph-parallel distributed analytics framework designed for graph processing tasks. It offers high scalability, fault tolerance, and flexibility in expressing graph algorithms. Inspired by the Bulk Synchronous Parallel (BSP) model, Pregel operates in super-steps, ena

0 views • 38 slides


Introduction to GraphLab: Large-Scale Distributed Analytics Engine

GraphLab is a powerful distributed analytics engine designed for large-scale graph-parallel processing. It offers features like in-memory processing, automatic fault-tolerance, and flexibility in expressing graph algorithms. With characteristics such as high scalability and asynchronous processing,

0 views • 26 slides


Data Processing and Analysis for Graph-Based Algorithms

This content delves into the preprocessing, computing, post-processing, and analysis of raw XML data for graph-based algorithms. It covers topics such as data ETL, graph analytics, PageRank computation, and identifying top users. Various tools and frameworks like GraphX, Spark, Giraph, and GraphLab

0 views • 8 slides


Understanding Graph Algorithms for Connectivity and Shortest Paths

Graph algorithms play a crucial role in solving problems represented as networks, maps, paths, plans, and resource flow. This content delves into ways to find connectivity in graphs and algorithms for determining shortest paths. It discusses graph representations using adjacency matrices and lists,

1 views • 32 slides


Insights into Cliques and Independent Sets in Graph Theory

Exploring the concepts of cliques, independent sets, and theorems in graph theory regarding enemy relationships, maximum number of edges in 3-free graphs, and properties of multipartite graphs. The propositions and theorems discussed shed light on graph structures and their properties, providing val

0 views • 32 slides