Graph Data Science with Neo4j (Estelle Scifo)（Z-Library）

Author： Estelle Scifo

Unlock the power of your data with Neo4j: the leading graph database for data science and machine learning applications. Key Features Learn how to deal with a graph database. Extract meaningful information from graph data. Use Graph Algorithms into a regular Machine Learning pipeline in Python. Book Description Neo4j and its Graph Data Science Library is a complete solution to store, query and analyze graph data. Graph databases are getting more popular among developers, which means data scientists are likely to face such databases in their future career. Moreover, graph algorithms are a trending topic which enable extracting context information and improve overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and its Graph Data Science Library. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running. Complete with step-by-step explanations of concepts and practical examples. You will begin by querying Neo4j with Cypher and characterize graph datasets. You’ll learn how to run graph algorithms on graph data stored into Neo4j, understand the core principles of the Graph Data Science Library to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you will be able to include graph algorithms into your normal ML pipeline. By the end of this book, you will be able to take advantage of the relationships in your dataset to improve your current model and make other types of prediction. What you will learn Querying graph databases such as Neo4j using the Cypher query language. Build graph datasets from your own data and public knowledge graphs. Extract new kind of features thanks by connecting observations. Make graph-specific predictions such as link prediction. Build a graph data science pipeline with Neo4j. Who This Book Is For Data Scientists and data profe

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【One-Line Pitch】 Unlock the power of your data with Neo4j: the leading graph database for data science and machine learning applicatio… 【Book Arc】 - **Opening (~0%–12%)**: Key Features Learn how to deal with a graph database.; For instance, words in menus or dialog boxes appear in bold. - **Early (~12%–35%)**: lication documentation to find out the proper instructions.; Compared to our previous try, we have created fewer nodes (5,120 instead of 6,978). - **Middle (~35%–65%)**: This can be achieved with the following code.; ure, also known as graph embedding. - **Late (~65%–88%)**: In this section, we are going to reproduce the visualizations displayed previously.; , pos pos) This is illustrated in the following screenshot: show at the same time (limited to 10,000). - **Ending (~88%–100%)**: IDs of nodes that need to be connected.; you will obtain slightly different results when running the code on your own. 【Key Takeaways】 - **Key Features Learn how…** (Opening): Key Features Learn how to deal with a graph database. - **For instance** (Opening): For instance, words in menus or dialog boxes appear in bold. - **ing graph databases** (Opening): ing graph databases, let’s introduce the concept of graphs. - **lication documentation…** (Early): lication documentation to find out the proper instructions. - **Compared to our previous try** (Early): Compared to our previous try, we have created fewer nodes (5,120 instead of 6,978). - **Then, use the search b…** (Early): Then, use the search bar to find the item of interest for you. 【Reading Tips】 - Use Passage locations below to jump into the text and set reading anchors - If this is a brief outline, click Regenerate (top right) for a synthesized guide 【Coverage Limits】 Compressed outline without the model (~33 index chunks). Full structured guide needs AI available.

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Make graph-specific predictions such as link prediction. Build a graph data science pipeline with Neo4j. Who This Book Is For Data Scientists and data pro...

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bed by our dataset: Movies TV shows Directors Actors Genres We can create a graph schema similar to the one displayed in the following figure: Figure 2.1 – S...

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llowing chapters: Chapter 3, Characterizing a Graph Dataset Chapter 4, Using Graph Algorithms to Characterize a Graph Dataset Chapter 5, Visualizing Graph Da...

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the procedure call means we are including all relationships graphName, as specified in the procedure CALL statement The number of nodes included in the proje...

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his case means natural with respect to the projected graph. So, using the default configurations everywhere, we end up using oriented relationships, and we a...

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ce of edges by changing their color. Setting the edge color In order to configure the edge color, the steps are almost identical to the nodes case: 1. From t...

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ws (the number of nodes) and where each column is a feature? The first approach is to use the adjacency matrix of the graph. Quickly introduced in Chapter 1,...

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ible to apply some preprocessing algorithms, such as scaler. To do this, we would need to use the addNodeProperty function, where the first parameter is the ...

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