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Simple text and photographs introduce the concept of graphing and present examples of two different kinds of graphs.

Nan surveys her class to find out what types of pets they have. See how she creates a bar graph to share her results.

Dan, Emma, and Ron want to compare how many apples they picked. Look at the picture graph to tell who picked the most.

Packed with 101 fun, colorful, and helpful anchor charts, this ready-to-use handbook for elementary teachers includes charts for such topics as the first weeks of school, reading, writing, spelling, behavior, and so much more.

Mr. Hall surveys his class to find out how many people walk, take the bus, or take a car to get to school. Watch as he makes a circle graph with his data.

This book is targeted at R programmers who want to learn the graphing capabilities of R. This book will presume that you have working knowledge of R.

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.

If you do systems administration work of any kind, you have to deal with the growing complexity of your environment and increasing demands on your time. Automating System Administration with Perl, Second Edition, not only offers you the right tools for your job, but also suggests the best way to approach specific problems and to securely automate recurring tasks. Updated and expanded to cover the latest operating systems, technologies, and Perl modules, this edition of the "Otter Book" will help you: Manage user accounts Monitor filesystems and processes Work with configuration files in important formats such as XML and YAML Administer databases, including MySQL, MS-SQL, and Oracle with DBI Work with directory services like LDAP and Active Directory Script email protocols and spam control Effectively create, handle, and analyze log files Administer network name and configuration services, including NIS, DNS and DHCP Maintain, monitor, and map network services, using technologies and tools such as SNMP, nmap, libpcap, GraphViz and RRDtool Improve filesystem, process, and network security This edition includes additional appendixes to get you up to speed on technologies such as XML/XPath, LDAP, SNMP, and SQL. With this book in hand and Perl in your toolbox, you can do more with less -- fewer resources, less effort, and far less hassle.

Readers will discover line graphs through examples that include waiting to ride a roller coaster, recording sales at a lemonade stand, and counting clouds. Colorful graphs teach readers, while fun illustrations keep their attention. Activities help readers explore the topic further.