Final Exam: Graph Analytics

Final Exam: Graph Analytics

• Use graph nodes and edges to model entities and relationships
• employ graph nodes and edges to model entities and relationships
• describe the use graph nodes and edges to model entities and relationships in the real world
• Recall the attributes of the property graph model used to represent knowledge graphs
• Model graphs using an adjacency list and adjacency set and compare the two representations; Represent graphs using an adjacency list in Python; Represent Graphs using an adjacency set in Python
• Represent Graphs using an adjacency set in Python
• Represent Graphs using an adjacency matrix in Python
• recall how depth-first and breadth-first traversal works; implement breadth-first traversal using a queue data structure; implement depth-first traversal using a stack, as well as using recursion
• compute the shortest path in a weighted graph using greedy traversal and the distance table
• recall the properties of greedy algorithms
• describe the structure and components of a graph recognize the different types of graphs based on the relationships between the nodes
• describe the properties and features of the Neo4j graph database
• set up Neo4j Desktop on your machine; create a database management system from a dump file in Neo4j Desktop; recognize the features including supporting apps which are available when using Neo4j Desktop
• use the Neo4j Browser to run simple queries using the Cypher query language
• recognize how data can be grouped in projects, database management systems, and databases
• use the Cypher shell to create and manage databases in a DBMS; create query parameters and execute Cypher queries from the Cypher shell
• enable and disable HTTP communication with a Neo4j DBMS and configure the communication ports
• use the Neo4j browser to create a new user and assign a built-in role to it
• recognize how frequently-run queries can be saved and organized from the Neo4j browser
• describe the use cases as well as the basic syntax of the Cypher query language
• provision nodes with labels as well as properties using the CREATE clause in a Cypher query
• define relationships which have their own properties using the Cypher language
• remove unwanted nodes and relationships in a Neo4j graph
• a variety of MATCH and OPTIONAL MATCH operations when searching for patterns
• recognize the use cases of the MERGE clause of a Cypher query
• use the Cypher query language to look for 2nd degree and higher degree connections between two nodes in a Neo4j database
• perform union and intersect operations on data in a Neo4j database using the Cypher query language
• sort the results of a query execution using the ORDER BY clause
• demonstrates searching for specific nodes in a database using the Bloom search bar
• configure the appearance of nodes and relationships in a Neo4j Bloom scene
• describe the various data views available in the Bloom user interface, such as the hierarchical and the presentation views
• use the Neo4j Bloom interface to analyze the nodes in your graphs, including the connections between them
• recognize the similarities and differences of data modeling approaches for relational, document and graph data
• use labels and properties for Neo4j nodes in an optimal manner from the point of view of anticipated queries
• describe how data in a tabular structure containing many-to-one relationships can be modelled as a Neo4j graph
• map the tables in a relational database to a graph structure using the Neo4j ETL tool
• redefine the nodes and relationships in your Neo4j database using the APOC library
• migrating to Aura with a dump file or using push-to-cloud
• write a Python application to modify and read from the contents of an Aura database
• connect to an Aura database using the Cypher shell and run queries against it
• install the Graph Data Science library for a Neo4j DBMS
• Create an in-memory graph using the native projection configuration for nodes and relationships
• Load properties from a source database to an in-memory graph
• build a sub-graph containing a subset of elements from an already existing graph
• add properties to an in-memory graph based on the computation of an algorithm
• load properties from the source database of a graph when exporting it to a new database
• export in-memory graphs to a set of CSV files containing data for nodes and relationships
• create nodes and relationships from the contents of CSV files
• find individual nodes or clusters of nodes in a network which are not connected to one another
• create a graph where each relationship has an attached weight
• perform a breadth-first and depth-first traversal of a graph
• outline Apache Hadoop and its ecosystem, describe GraphFrames and their capabilities, and recognize where GraphFrames fit into the Apache Hadoop ecosystem
• demonstrate the identification of the most and the least-connected nodes in a graph
• search for patterns of relationships between the nodes in a Spark GraphFrame
• use the breadth-first search and the shortestPaths functions to find the shortest paths between nodes in a graph
• describe the different operations performed by individual neurons in a layer of a neural network
• set up the Python libraries required to use the Spektral library for building a graph neural network (GNN)
• outline graph convolutional networks (GCNs) and recognize the operations performed on input data when using a GCN, including symmetric normalization
• recognize the structure required to feed graph data into a graph convolutional network (GCN) model
• identify various factors which can influence the quality of predictions made by a GCN model

Final Exam: Graph Analytics will test your knowledge and application of the topics presented throughout the Skillsoft Aspire Graph Analytics Journey.

Graph Modeling on Apache Spark: Working with Apache Spark GraphFrames

Graph Modeling on Apache Spark: Working with Apache Spark GraphFrames

• discover the key concepts covered in this course
• outline Apache Hadoop and its ecosystem, describe GraphFrames and their capabilities, and recognize where GraphFrames fit into the Apache Hadoop ecosystem
• download and install Apache Spark and set up your IDE with GraphFrames
• construct a GraphFrame starting with the definition of its nodes and edges
• define functions to present a directed as well as an undirected graph
• demonstrate the identification of the most and the least-connected nodes in a graph
• apply filters on the nodes in a graph at the DataFrame and the GraphFrame levels
• apply filters on the edges of a graph and apply aggregation operations on them
• search for patterns of relationships between the nodes in a Spark GraphFrame
• illustrate how to find chains of connections as well as cycles in a GraphFrame
• use the breadth-first search and the shortestPaths functions to find the shortest paths between nodes in a graph
• apply the PageRank algorithm to identify triangles of connections in a graph and calculate the page rank for a graph of connected web pages
• summarize the key concepts covered in this course