Graph data processing - PowerPoint PPT Presentation


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


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


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


Opportunities in Ethiopia's Agro-Processing Industry

Ethiopia stands out as a leader in raw material production for agro-processing industries, offering opportunities in dairy, juice processing, edible oil processing, poultry, beef production, and tomato processing. With abundant resources, suitable climate conditions, and a growing domestic market, E

0 views • 8 slides


Combining Graph Algorithms with Data Structures and Algorithms in CSE 373 by Kasey Champion

In this lecture, Kasey Champion covers a wide range of topics including graph algorithms, data structures, coding projects, and important midterm topics for CSE 373. The lecture emphasizes understanding ADTs, data structures, asymptotic analysis, sorting algorithms, memory management, P vs. NP, heap

0 views • 38 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


Evolution and Demise of Freebase and the Google Knowledge Graph

Freebase was launched in 2005 as an open database of knowledge, initially populated with Wikipedia data and later incorporating crowdsourced updates. Acquired by Google in 2010, it was transitioned into Google's Knowledge Graph before being decommissioned in 2016. The schema contained around 1500 ty

4 views • 12 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


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


FlashGraph: Processing Billion-Node Graphs on Commodity SSDs

FlashGraph proposes a system that combines SSDs and RAM for efficient graph processing, storing vertices in memory and edge lists in SSD storage. The system can handle large graphs without using excessive memory and boasts performance comparable to in-memory graph processing engines. While SSDs offe

0 views • 6 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


Exploring Graphs: Visualizations and Representations in Java

Delve into the world of graphs with a focus on visualizations for networks and building up graph representations in Java. Explore different graph representations, adjacency lists, and key-value mappings, along with insights on storing and tracking data efficiently using Java data structures. Dive in

0 views • 12 slides


Enhancing Near-Data Processing with Active Routing

Explore the implementation and benefits of Active-Routing for efficient data processing in memory networks. Motivated by the increasing demands for memory in graph processing and deep learning, this approach aims to reduce data movement, energy consumption, and costs associated with processing large

0 views • 46 slides


Introduction to Pig Latin for Data Processing in Hadoop Stack

Pig Latin is a dataflow language and execution system that simplifies composing workflows of multiple Map-Reduce jobs. This system allows chaining together multiple Map-Reduce runs with compact statements akin to SQL, optimizing the order of operations for efficiency. Alongside Pig Latin, the Hadoop

0 views • 20 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


Introduction to Graphviz: A Powerful Visualization Tool

Graphviz is a versatile tool used to create visual representations of graphs by describing them in the DOT language. The process involves writing a text file with the graph description, using Graphviz to generate the graph picture, and then viewing or processing the output. It provides a detailed in

0 views • 10 slides


BiGraph: Bipartite-Oriented Distributed Graph Partitioning for Big Learning

BiGraph is a distributed graph partitioning algorithm designed for bipartite graphs, offering a scalable solution for big data processing in Machine Learning and Data Mining applications. The algorithm addresses the limitations of existing partitioning methods by efficiently distributing and managin

0 views • 45 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


Ligra: A Lightweight Graph Processing Framework for Shared Memory

Ligra is a lightweight graph processing framework developed by Julian Shun during his time at the Miller Institute, UC Berkeley. This framework, created in collaboration with Laxman Dhulipala and Guy Blelloch, is designed for shared memory systems to efficiently analyze large graphs. Key features in

0 views • 21 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 Graph Data Structures and Algorithms by Ali Akbar Mohammadi

This content delves into the foundational concepts of graph data structures, covering topics such as graph traversal, transitive closure, minimum spanning trees, and more. Ali Akbar Mohammadi provides insight into the world of graphs, emphasizing the importance of vertices, edges, and the relationsh

0 views • 19 slides


Understanding Transaction Processing Systems (TPS)

Transaction Processing Systems (TPS) are vital components in capturing, storing, and processing data generated from various business transactions. They ensure efficient handling of high volumes of data while maintaining accuracy, security, and privacy. TPS operate through automated data entry, batch

0 views • 24 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


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


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


Understanding Edge Computing for Optimizing Internet Devices

Edge computing brings computing closer to the data source, minimizing communication distances between client and server for reduced latency and bandwidth usage. Distributed in device nodes, edge computing optimizes processing in smart devices instead of centralized cloud environments, enhancing data

0 views • 32 slides


Insight into PEPS Data Processing Architecture by Erwann Poupard

Erwann Poupard, a Software Ground System Engineer at CNES, Toulouse, France, plays a crucial role in the PEPS data processing architecture. The outline covers PEPS HPSS data storage statistics, current data processing trends, and future plans including PEPS V2 development. Explore PEPS processing ch

0 views • 8 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