Graph Analytics for Big Data Quiz Answers

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Graph Analytics for Big Data complete course is currently being offered by UC San Diego through Coursera platform.

About this Course

After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner.  Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

Skills You Will Gain

  • Graph Theory
  • Neo4j
  • Analytics
  • Graph Database

Also Check: How to Apply for Coursera Financial Aid

Graph Analytics for Big Data Quiz Answers - Coursera!

Quiz 1 – Introduction to Graphs

Q1) Which of the following are graphs? (check all that apply)

  •                                                          This is Correct 

Q2) Which of the following is the correct adjacency matrix for this graph? 


     This is Not Correct 

  •  Neither option is correct.
Q3) Which of the following content would be objects (or nodes) in a graph that represents the activity in a facebook page?

  • Created_post (the action of creating a post)
  • friends (the action of making someone your friend)

Q4) Based on the videos, which kinds of analysis might one be able to perform on a tweet graph?

  • extract conversation threads
  • find interacting groups of users
  • find influencers in a twitter community

Q5) The key reason mentioned in the video that biology applications need Big Data analytics is…

  • The complexity of interactions that correlate to inform phenotypes.
  • The new use of computational techniques to explore new areas of biology research more quickly than can be done with “live” or wetlab experiments.
  • The integration of multiple data sources from different researchers and of different sources of information.

Q6) Which of the Vs BEST describes the result in constant increasing in the number of edges in a graph, sometimes causing challenges in knowing when one has found “an answer” to one’s analysis question?

  • Velocity
  • Valence
  • Variety
  • Volume

Q7) Which of the Vs results in increased algorithmic complexity (which can cause analyses to not be able to finish running in reasonable amounts of time)?

  • Valence
  • Velocity
  • Volume
  • Variety

Q8) Which of the Vs results in challenges due to graphs created from varying kinds, formats, sources, and meanings of data?

  • Variety
  • Valence
  • Volume
  • Velocity

Q9) Which of the Vs causes increased interconnectivity of a graph — which can cause problems in analysis due to density?

  • Velocity
  • Variety
  • Volume
  • Valence

Q10) Updating a graph with a stream of posting information on facebook is an example of which of the Vs?

  • Variety
  • Valence
  • Velocity
  • Volume

Q11) Studying Amarnath’s gmail interactions over time (as gmail started to be used by more and more people) is BEST defined as an impact of which of the Vs?

  • Valence
  • Velocity
  • Variety
  • Volume


Quiz 2 – Graph Analytics Applications

Q1) A graph representing tweets would have only “one type” (e.g. label) of node.
Q4) In an email network, which might reasonably be represented by weight on edges?

  •  the total number of emails sent by one user in a week
  •  the total number of people who sent an email in a week
  •  average number of emails sent from one user to another in a week

Q5) A loop in a graph is where:

  •  where there is an edge from a node to itself.
  •  when there is a edge from A->B, there is also an edge from B->A.
  •  where there is a path in some way from a node, through 1 or more other – nodes, back to the original node.
Q7) When trying to represent a relationship between Maria and Julio who have more than one relationship to each other (e.g., tennis partner, co-worker, emergency contact) which of the following would be needed in a graph representing those relationships

  •  Separate graphs for each kind of relationship
  •  Multiple nodes for each of Maria and Julio, to capture the various relationships
  •  Multiple edges between Maria and Julio
Q9) Trails (paths without repeated edges) can be interesting in which of the following problem applications?

  •  Routing to avoid visiting the same city.
  •  An email network tracing email replies.
  •  Routing to avoid using the same bridge or road.
  •  An email network tracing frequency of emails from one person to another.
Q14) Which are the two kinds of constraints on paths discussed in the video on basic path analytics? (check 2) Hint: remember the example of Amarnath needing to get to work by taking his son to school.

  • Inclusion of nodes and/or edges
  • Exclusion of nodes and/or edges

Q15) What are examples of preference constraints in the Google Maps application?

  • Avoid roads under construction

Q17) In the video on “Inclusion and Exclusion Constraints” we learn that adding constraints can actually make our analysis job easier. For example, when we require that a given node be included on a path, which of the following impacts now make the analysis job easier? 

  • Reduction of the size of the graph
  • Splitting the task into 2 independent shortest path problems
  • Changing the weights on the edges of the graph and/or subgraphs

Quiz 3 – Connectivity, Community, and Centrality Analytics

Q1) The example given in the lectures of when a power network loses power in large portions of its service area was an example of what?

  •  a problem that can occur when centrality is too high
  •  an attack which causes disconnection of the graph
  •  high levels of connectivity which make it easy to bring a network down
Q4) If you were going to look for a node which would be most likely to be the target of an attack to disconnect a network, what would be the best characteristic to look for?

  • nodes that, if they were removed, would cause the graph to go from strongly connected to weakly connected
Q6) In the graph below, which node is the greatest listener?

Q7) In the graph below, which nodes are the greatest communicators? (Hint: there’s a tie)

Q8) What would we be looking for if we followed the steps below? Note: we have 2 graphs.

Create a table for each graph where, for each node, you list the degree of the node. For each graph, create a histogram indicating how many nodes in that graph have a specific degree (e.g., how many nodes have degree 1? 2? etc.). Use advanced approaches (e.g. Euclidean distances) to compare these two histograms.

Q9) Which of the following are the three type of analytics questions asked about communities?
Q10) What type of community analytics question is the following?

Did a community form on twitter around the 2014 World Cup in Brazil?
Q11) Which type of community analytics question is the following?

How tightly knit was the 2014 World Cup twitter community on July 13, 2014 (the day of the finals)?
Q13) Which of the two graphs below is more modular?

Q14. Which of the following community tracking phases usually occurs when a company spins off a start-up?
Q15) An influencer in a network is defined as:

  •  the biggest gossip in the network
  •  a node which can reach all other nodes quickly
  •  a node which has heavy weight edges to at least 1/2 of the nodes in the network

Q16) Which of the following are the 2 core “key player” problems that centrality analytics can address?

  •  What is the shortest path through a network
  •  A set of nodes which can reach (almost) all other nodes
  •  Which nodes’ removal will maximally disrupt the network
  •  Which nodes have the highest ratio of out-degree nodes to in-degree nodes
Q18) What kind of centrality would you want to analyze in a graph if you wanted maximize commodity flow in a network?
Q19) What kind of centrality identifies “hubness”?

Quiz 4 – Graph Analytics with Neo4j

Q1) Which of the following is a Cypher command used to combine two or more query results?

Q2) For a graph network whose nodes are all of type “MyNode”, which has both incoming and outgoing edges, and which has both root and leaf nodes, what will the following Cypher code return in a Neo4j report?

match (n:MyNode)<-[r]-() return n

  • All nodes except root nodes.
  • The entire network, all nodes and edges
  • All nodes and edges except leaf nodes and their edges.

Q4) The following query will return a graph containing whatever loops might exist.
Q9) Cypher includes a ‘diameter’ command to find the longest path in a graph network.

Quiz 5 – Assessment Questions on ‘Practicing Graph Analytics in Neo4j With Cypher’

Q1) What is the number of nodes returned?
Q5) The query match (n)-[r]->(m) where m <> n return distinct n, m, count(r) as myCount order by myCount desc limit 1 produces what?

  • a random edge
  • two neighboring nodes, each with a high outdegree
  • the node with the maximum number of looping edges
  • the pair of nodes with the maximum number of multi-edges between them

Q6) The query match p=(n {Name:’BRCA1′})-[:AssociationType*..2]->(m) return p produces what?

  •  The neighbors of the node whose name is ‘BRCA1’
  •  The 2-neighborhood of the node whose name is ‘BRCA1’
  •  The neighbors’ neighbors of the node whose name is ‘BRCA1’
  •  The neighbors whose distance is greater than 1 and less than 2 of the node whose name is ‘BRCA1’
Q9) Applying the example queries provided to you, create the degree histogram for the network. How many nodes in the graph have a degree of 3?

Quiz 6 – Using GraphX

Q1) In this code snippet below from the Hands On exercise on importing data, ‘100L + row…’ adds 100 to the value of every country ID. Which of the following statements are true regarding this decision? (Note: you may select more than one)

val countries: RDD[(VertexId, PlaceNode)] =

  sc.textFile(“./EOADATA/country.csv”).

    filter(! _.startsWith(“#”)).

    map {line =>

      val row = line split ‘,’

      (100L + row(0).toInt, Country(row(1)))

    }

  •  Another option would be to add 500 to the country keys.
  •  This step was needed to create unique keys between the country and the metropolis datasets.
  •  Another option would have been to add 100 to the metropolis keys as they were imported, and leave the country keys as they were originally numbered.

Q2) In the metro example, what is an in-degree in relation to a country? Hint: this was covered in the Building a Degree Histogram Hands On exercise.

  • A metro area or metropolis

Q3) In the Hands On exercise on network connectedness and clustering, Antarctica was easy to identify. Why?

  • It had a vertex ID of 205
  • It is the green dot that that has no connections, or it is the least connected cluster

Q4) In the Facebook graph example, the visualization looked like broccoli. Why?

  • In a directed graph, the stalks are large.
  • Social networks have communities or pockets of people who interact densely.
  • The high centrality of some people nodes in facebook gives the graph its broccoli shape.



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