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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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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Learn how to build artificial neural networks from scratch, focusing on multi-level feedforward networks like multi-level perceptrons. Discover how neural networks function, including training large networks in parallel and distributed systems, and grasp concepts such as learning non-linear function

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Attention is our ability to focus on specific stimuli while disregarding distractions. It can be categorized into focused, sustained, divided, and alternating attention. External and internal factors influence our attention levels. The nature of stimuli plays a crucial role in capturing attention ef

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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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This presentation provides an introduction to neural networks in IBM SPSS Modeler 14.2. It covers the concepts of directed data mining using neural networks, the structure of neural networks, terms associated with neural networks, and the process of inputs and outputs in neural network models. The d

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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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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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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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Graph data sets are prevalent in various domains like social networks and biological networks. Label-Constrained Reachability (LCR) queries aim to determine if a vertex can reach another vertex through specific labeled edges. Existing works utilize exhaustive search or graph indexing techniques, but

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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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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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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

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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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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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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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Explore the concepts of graph theory and management science, focusing on networks, spanning trees, and their practical applications. Learn about the difference between a snowplow tracing streets, a traveler visiting cities, and connecting towns with cables. Discover how networks like Facebook evolve

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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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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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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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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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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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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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Introducing the Contrastive Knowledge Graph Attention Network (C-KGAT) for personalized recipe recommendation, addressing user preferences and noise in interactions. The model leverages a collaborative knowledge graph, user-recipe interactions, and textual features to recommend top-K recipes to user

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Explore the differences between Whole Networks and Ego Networks in social network analysis. Whole Networks provide comprehensive information about all nodes and links, enabling the computation of network-level statistics. On the other hand, Ego Networks focus on a sample of nodes, limiting the abili

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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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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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Utilizing heterogeneous graph attention networks, this study addresses the limitations of existing user profiling methods by integrating multiple data types and capturing rich interactions in user data. The approach tackles critical problems in representation learning, information propagation, and m

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Attention Autism is a highly engaging method for developing attention and early communication skills, beneficial for all children. It involves motivating activities, shared experiences, and clear communication. The approach consists of stages such as focusing attention, sustaining attention, shiftin

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The text explores the concept of attention economy, emphasizing the scarcity of human attention in the information age. It delves into how attention has become a valuable commodity, driving global corporations to maximize engagement. Discussions cover distraction economics, the role of technology in

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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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Bayesian networks, also known as graphical models, provide a compact and efficient way to represent complex joint probability distributions involving hidden variables. By depicting conditional independence relationships between random variables in a graph, Bayesian networks facilitate Bayesian infer

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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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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

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In neural machine translation, attention mechanisms allow selective encoding of information and adaptive decoding for accurate output generation. By learning to align and translate, attention models encode input sequences into vectors, focusing on relevant parts during decoding. Utilizing soft atten

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Researchers at the National University of Singapore discuss the concept that a social network is not simply a graph, highlighting key differences such as dynamics, multi-dimensionality, and the challenges in link prediction. The analysis delves into the complexities of social network data compared t

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