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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Learn about algorithms, programming, and abstraction in computing. Explore the definition and properties of algorithms, the relationship between algorithms and programming, and the concept of abstraction. Discover how algorithms are like recipes and how abstraction simplifies complex tasks in comput

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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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Characterizing Distributed Systems: Multiple autonomous computers with CPUs, memory, storage, and I/O paths, interconnected geographically, shared state, global invariants. Classifying Distributed Systems: Based on synchrony, communication medium, fault models like crash and Byzantine failures. Comp

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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 (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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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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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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Near-Optimal Quantum Algorithms for String Problems by Ce Jin and Shyan Akmal presents groundbreaking research on string problem solutions using quantum algorithms. The study delves into various key topics such as Combinatorial Pattern Matching, Basic String Problems, Quantum Black-box Model, and mo

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Approximation algorithms aim to find near-optimal solutions for optimization problems, with the performance ratio indicating how close the algorithm's solution is to the optimal solution. The terminology used in approximation algorithms includes P (optimization problem), C (approximation algorithm),

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In parallel computing, processing elements collaborate to solve problems, while distributed systems appear as a single coherent system to users, made up of independent computers. Contemporary computing systems like mobile devices, IoT devices, and high-end gaming computers incorporate parallel and d

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Learn about various algorithms such as Dijkstra's, Bellman-Ford, and more for finding the shortest paths between all pairs of vertices in a graph. Discover pre-computation benefits and clever recurrence relationships in optimizing path calculations.

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A distributed system involves software components on different computers communicating through message passing to achieve common goals. Organized with middleware like RMI, it allows for interactions across heterogeneous networks. RMI facilitates building distributed Java systems by enabling method i

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Distributed DBMS reliability is crucial for ensuring continuous user request processing despite system failures. This chapter delves into fundamental definitions, fault classifications, and types of faults like hard and soft failures in distributed systems. Understanding reliability concepts helps i

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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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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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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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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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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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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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Discussion on fast, randomized mutual exclusion techniques by George Giakkoupis and Philipp Woelfel. Exploring asynchronous shared memory systems with atomic operations. Understanding mutual exclusion principles as outlined by Dijkstra in 1965 and measuring time efficiency in critical sections. Delv

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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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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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This study delves into Byzantine Fault Tolerance (BFT) protocols in the realm of distributed ledgers, exploring the complexities of achieving consensus in trusted adversarial environments. The research examines the classic problem in computer science where distributed nodes communicate to reach agre

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Explore the fundamental concepts of algorithms and programming through visual representations and practical examples. Learn about algorithmic thinking, abstraction, recipe-like algorithms, and the importance of logical steps in accomplishing tasks. Discover how algorithms encapsulate data and instru

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Distributed algorithms play a crucial role in leader election within anonymous systems where nodes lack unique identifiers. The content discusses the challenges and impossibility results of deterministic leader election in such systems. It explains synchronous and asynchronous distributed algorithms

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Delve into the intricacies of networking principles and routing algorithms in distributed systems. Explore the four layers studied, including the network layer that handles routing. Discover the role of routers in forwarding packets between networks and the challenges of designing routing algorithms

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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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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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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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This paper discusses constant-time algorithms for sparsity matroids, focusing on (k, l)-sparse and (k, l)-full matroids in graphic representations. It explores properties, testing methods, and graph models like the bounded-degree model. The objective is to efficiently determine if a graph satisfies

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Graph biconnectivity is a crucial concept in network analysis, ensuring connectivity even when vertices are removed. Efficient distributed biconnectivity algorithms have practical applications in identifying single points of failure in networks. Leveraging previous work on Ice Sheet Connectivity, a

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This research introduces a groundbreaking distributed memory multi-GPU graph coloring implementation, achieving significant speedups and minimal color increase. The approach enables efficient coloring of large-scale graphs with billions of vertices and edges. Additionally, the study explores the pra

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Pseudodeterministic algorithms provide a unique approach to the search problem associated with binary relations, offering an error reduction technique while sacrificing the ability to approximate the average value of a function. By introducing m-pseudodeterministic and pseudo-pseudodeterministic alg

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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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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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Distributed systems encompass a range of technologies aimed at improving storage efficiency and reliability. This includes RAID (Redundant Array of Inexpensive Disks) strategies such as RAID levels, Lustre Linux Cluster for high-performance clusters, MogileFS for fast content delivery, and HDFS (Had

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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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Distributed software engineering plays a crucial role in modern enterprise computing systems where large computer-based systems are distributed over multiple computers for improved performance, fault tolerance, and scalability. This involves resource sharing, openness, concurrency, and fault toleran

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PSync is a language designed by Cezara Drăgoi, Thomas A. Henzinger, and Damien Zufferey to simplify the implementation and reasoning of fault-tolerant distributed algorithms. It introduces a DSL with key elements like communication-closed rounds, an adversary environment model, and efficient runtim

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