Graph algorithms - 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

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Understanding Greedy Algorithms and Minimum Spanning Trees

Greedy algorithms build solutions by considering objects one at a time using simple rules, while Minimum Spanning Trees find the most cost-effective way to connect vertices in a weighted graph. Greedy algorithms can be powerful, but their correctness relies on subtle proofs and careful implementatio

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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

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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

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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

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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

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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

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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

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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.

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Understanding Randomized Algorithms: A Deep Dive into Las Vegas and Monte Carlo Algorithms

Randomized algorithms incorporate randomness into computations, with Las Vegas algorithms always providing the correct answer but varying in time, while Monte Carlo algorithms occasionally give wrong answers. Quick Sort is a classic Las Vegas algorithm that involves pivoting elements for sorting. Ch

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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

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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

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Graph Connectivity and Single Element Recovery via Linear and OR Queries

The content discusses the concepts of graph connectivity and single element recovery using linear and OR queries. It delves into the strategies, algorithms, and tradeoffs involved in determining unknown vectors, edges incident to vertices, and spanning forests in graphs. The talk contrasts determini

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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

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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

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Mathematical Analysis of Algorithms in CMPE371 - Fall 2023-2024

Explore the mathematical analysis of algorithms in CMPE371 for Fall 2023-2024, focusing on non-recursive and recursive algorithms. Learn how to analyze non-recursive algorithms by deciding on input size parameters, identifying basic operations, and simplifying summations. Dive into recursive algorit

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Distributed Graph Algorithms: Introduction and Tree Coloring

This class introduces the fundamentals of distributed graph algorithms focusing on network modeling, complexity measures, solving graph problems, and comparing distributed vs. centralized algorithms. It covers topics such as the LOCAL model, synchronous rounds, communication rounds, computation time

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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

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Computation on Graphs: Maximal Independent Sets

The content discusses the concept of maximal independent sets in graph theory. It defines independent, maximal, and maximum sets, highlighting the difficulty in finding a maximum independent set due to its NP-hard nature. Sequential and parallel algorithms for finding maximal independent sets are pr

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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

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Understanding Minimal Spanning Trees in Graph Theory

Dive into the concept of minimal spanning trees in graph theory with a focus on algorithms like Prim's and Kruskal's. Explore the definition of trees, spanning trees, and weighted graphs. Learn about the importance of finding the minimal spanning tree in a graph and how it contributes to optimizatio

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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

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Exploring the Role of Algorithms in Game Design

Delve into the world of algorithms in game design, from understanding the fundamental concept of algorithms to their pervasive presence in various aspects of gaming, such as military simulations, medical simulations, and gameplay mechanics. Explore how algorithms shape experiences in different types

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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

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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

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Graph Analysis Techniques and Algorithms

Graph analysis involves utilizing different algorithms for parallelizing activities and performing operations like relational joins efficiently in large graphs with small diameters. Techniques such as dividing graphs into communities based on edge betweenness are explored. Breadth-first search is ap

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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

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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

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Implementing Iterative Algorithms with SPARQL

This comprehensive guide explores the implementation of iterative algorithms with SPARQL, focusing on YarcData/Cray's approach to using these algorithms. It covers YarcData's interest in graphs, the Urika appliance, iterative algorithms in machine learning, implementation approach, and algorithms im

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Understanding Algorithms and Sorting Methods

Algorithms are precise instructions used to solve problems efficiently, especially in computer operations. Searching algorithms like tree and graph search are essential, while sorting algorithms such as quick sort and bubble sort help organize data effectively.

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Understanding Sublinear Algorithms and Graph Parameters in Centralized and Distributed Computing

Centralized sublinear algorithms and their relation to distributed computing are explored, emphasizing the efficiency of algorithms in processing large inputs in sublinear time. Examples of sublinear algorithms for various objects are provided, along with the computation and approximation of graph p

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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

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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

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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

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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

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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

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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

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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

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Understanding Depth-First Search in Graph Algorithms

Delve into the world of graph algorithms and explore Depth-First Search (DFS) in both undirected and directed graphs. Learn about tree edges, back edges, forward edges, and cross edges, along with the terminology associated with DFS trees. Discover how to detect back edges and perform a depth-first

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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,

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