Final Exam: Graph Analytics

WHAT YOU WILL LEARN

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  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
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  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