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

System models play a crucial role in software engineering, aiding in understanding system functionality and communicating with customers. They include context models, behavioural models, data models, object models, and more, each offering unique perspectives on the system. Different types of system

1 views • 33 slides

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 (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

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

Models of teaching serve as instructional designs to facilitate students in acquiring knowledge, skills, and values by creating specific learning environments. Bruce Joyce and Marsha Weil classified teaching models into four families: Information Processing Models, Personal Models, Social Interactio

1 views • 28 slides

This content delves into the Breadth-First Search (BFS) algorithm, a fundamental graph searching technique. It explains the step-by-step process of BFS, from initializing the graph to traversing vertices in a specific order. Through detailed visual representations, you will gain insights into how BF

1 views • 75 slides

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

Explore the intriguing concepts of the Friendship Theorem and Freshman's Dream in graph theory along with examples and visual illustrations. Learn about common friends, relationships between vertices and edges, and what defines a graph in a concise yet comprehensive manner.

0 views • 84 slides

Models play a crucial role in various disciplines, including agricultural geography, by offering a simplified and hypothetical representation of complex phenomena. When used correctly, models help in understanding reality and empirical investigations, but misuse can lead to dangerous outcomes. Longm

0 views • 8 slides

Graph Theory Matchings have a rich history dating back to the 9th century AD. Distinct Representatives and Hall's Theorem play important roles in determining matchings in graphs. Understanding concepts like bipartite graphs, maximum matchings, and Hall's Marriage Theorem is essential in graph theory

1 views • 12 slides

Freebase was initially created in 2005 as an open shared database of knowledge, later acquired by Google and absorbed into the Google Knowledge Graph. Its approach included crowdsourcing updates and additions, focusing on data rather than text. The schema of Freebase included around 1500 types, 3500

1 views • 12 slides

Explore the process of mapping relational data to RDF, including the choice of RDF vocabulary, defining mapping techniques, and exporting strategies. Learn about RDB systems that support RDF, direct mapping approaches, and the use of hybrid storage solutions. Discover how to bridge SPARQL and SQL fo

1 views • 13 slides

Discrete Optimization is a field of applied mathematics that uses techniques from combinatorics, graph theory, linear programming, and algorithms to solve optimization problems over discrete structures. This involves creating mathematical models, defining objective functions, decision variables, and

0 views • 12 slides

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

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

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

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

Explore the intriguing world of Radio Direction Finding (RDF), an essential technique for determining the direction of radio signals. From its origins with Heinrich Hertz in 1888 to its pivotal role in military operations during WWI and WWII, RDF technology has evolved over the years while still rel

9 views • 23 slides

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

This content delves into concepts related to Semantic Web, RDF (Resource Description Framework), and formalisms of Trust Reasoning Representation. It covers topics such as RDF resource description, triple models, machine interpretation challenges, and the role of key figures like Timothy John Berner

0 views • 38 slides

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

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

Explore Session 3 of the Open Provenance Model Tutorial, covering topics like the XML Schema for OPM, OWL ontology, OPM Layered Architecture, XML Binding, RDF Binding, and more. Dive into OPM Layered Model, XML Binding, OPM Graph, OPM Process, and OPM Artifact to understand specialized domains and e

0 views • 22 slides

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 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

Define lossless bi-directional transformations, complete FHIR coverage, enforce constraints, enable inference, and ensure RDF quality in representing FHIR resource instances. Support vocabulary bindings, annotation information, and datatype IRIs while ensuring auto-generatability of mappings.

0 views • 5 slides

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 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

EEKE2021 presented a model that integrates entity description and network structure to enhance knowledge graph completion (KGC). The model aims to predict missing information in the graph by considering various sources of data beyond the traditional triple structure. It discusses related work, resea

0 views • 22 slides

Explore the significance of metadata in data management, the use of RDF and triple stores in organizing data, different reasoning methods available, and the importance of metadata structures. Learn how schemas in RDF allow for easy integration of diverse data types without requiring database reorgan

0 views • 18 slides

Delve into the world of Linked Data with topics ranging from its fundamental principles like URIs, RDF, and Triples to practical aspects like publishing data using standard web technologies such as HTTP. Explore the essence of RDF graphs, the significance of naming things with URLs, and ways to prov

0 views • 26 slides

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

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

Resource Description Framework (RDF) is a key data model for the Semantic Web, providing a standard way to represent knowledge through subject-predicate-object triples. RDF serves as the foundation for various knowledge representation languages and ontology tools on the web. Through RDF, relationshi

0 views • 36 slides

This research paper presents a mechanized model for Content Addressable Network (CAN) protocols, focusing on supporting RDF data storage in the Semantic Web. It discusses the general motivation, CAN principles, RDF queries, and challenges in handling queries over variables in large-scale settings. T

0 views • 26 slides

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

Explore the intersection of RDA and Linked Data through insightful discussions on leveraging URIs, RDF graph models, and the potential for machine interoperation of library data. Delve into the concept of encoding data in a graph format and the vast possibilities it opens up in the information commu

0 views • 16 slides

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

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