A book created to give students the practic they need in a fun format.

This graph art activity book is a compilation of holiday pictures which are designed to fit graph paper squares. The child colors in the squares on graph paper according to the direction sheet, and a mystery picture appears.

Here’s a super-fun, kid-pleasing way to introduce and reinforce graphing! Your students will love creating graph art pictures like Wiggle Worm, Mystery Letter, and What’s Hatching? as they practice making simple bar and line graphs, and build skills in addition and subtraction. Fully reproducible! For use with Grades 1-2.

Aimed at "the mathematically traumatized," this text offers nontechnical coverage of graph theory, with exercises. Discusses planar graphs, Euler's formula, Platonic graphs, coloring, the genus of a graph, Euler walks, Hamilton walks, more. 1976 edition.

It's never too early to introduce your elementary child or students to the joys of graph art! Learn the basics with simple language, fun and easy graphs, and increasing level of difficulty throughout the book. This elementary edition will help your students master graphing skills at their own pace, working with familiar ABC letters, animals, basic decimals, and eventually adding challenge with Quadrants 2, 3 and 4. It provides unlimited copy rights within the teacher's own classroom. Complete your Graph Art collection by purchasing the middle school and advanced editions as well! In addition to 47 unique graph art puzzles, each section of this book contains instructional modules, vocabulary, practice pages, and a teacher key section at the end. Copies of blank graph paper masters are also included. This book is written by a teacher for teachers; in student-friendly language, while building the foundation of a sound mathematical vocabulary. Students will be inspired to create, explore, and challenge themselves in a way they have never done before. Adults will be thrilled at the ease of its use. A must-have for all Cartesian Plane enthusiasts.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.