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# Social Network Analysis: Twitter Network Quiz

INSTRUCTION: Open quiz.html or quiz.Rmd to see the plot

## Graph theory and SNA

This section will assess your understanding of Graph theory and SNA

1. Which statement is TRUE about graph theory & SNA: (a) Network can be divided into 2 types: directed and undirected. (b) SNA is basicly an implementation of graph theory in human social life. (c) In-degree in directed network, is a two-way relationship between nodes. usually drawn with lines without arrows. (d) Social Network Analysis can be implemented in wide areas, including finding information networks within terrorist group.

• [ ] (a), (b), and (c)
• [ ] (a), (b), and (d)
• [ ] (a), (c), and (d)
• [ ] all of the above
2. From the picture below. Calculate the shortest path between nodes 4 to nodes 13

• [ ] 6
• [ ] 5
• [ ] 7
• [ ] 8

## SNA metrics (centrality & modularity)

1. Closeness centrality calculate shortest path from specific nodes to every nodes in the network. Thus the more central a node is, the closer it is to all other nodes. From the statements below, choose the correct intepretation of closeness centrality.
• [ ] nodes with high closeness centrality tend to be connected with similliar nodes that also have high closeness centrality value.
• [ ] nodes with high closeness centrality connect two separated network (subgraph). if the nodes disappear, communication between subgraph will not be possible
• [ ] nodes with high closeness centrality will spread information faster than any nodes. Its because they have the average length of shortest path to every nodes in network.

Question 4 - 5 Run this code in your workspace

``````qz <- read.csv("data_input/qzexample.csv")
``````

From the dataframe, build nodes & edges dataframe then create directed network. Name your network as "network_qz"

``````# your code here
# make sure you add as_tbl_graph
nodes_qz <-
edges_qz <-

network_qz <-
``````

with igraph package, build network community using cluster_walktrap() function then calculate the modularity.

``````# your code here
``````
1. Select the correct statements based on your output
• [ ] High Modularity, the community in network are well separated. they have dense connections between the nodes within community but sparse connections between nodes in different community
• [ ] Low Modularity, the community in the network actually don't have much difference. Nodes in different community have dense connection. The algorithm having a hard time separating the nodes, will be much better if we use another community detection algorithm
• [ ] High Modularity, the community in network are well separated. they have sparse connections between the nodes within community but dense connections between nodes in different community
• [ ] Low Modularity, the community in the network have a lots of difference but the nodes in same community also have sparse connection.
``````# this plot will help you estimate the centrality value
plot(network_qz, edge.arrow.size = 0.5, layout = layout_with_fr)
``````
1. Calculate betweenness, closeness, degree centrality and select which node have the highest centrality value consecutively
• [ ] 8, 4, 5
• [ ] 7, 5, 5
• [ ] 7, 4, 2
• [ ] 8, 5, 5

## Visualization

Question 6-7 Run this code in your workspace NOTE: always use set.seed(123) in the top of your chunk

``````set.seed(123)
small_qz <- play_smallworld(1,250,3,0.05)
``````

Visualize the network using this characteristic: (you need to do this in order) in top of your chunk, make sure to input set.seed(123) create nodes name by row_number() and create network community using group_louvain() algorithm using mutate() ilter the nodes that only appear in community 1:2 using mutate(), calculate eigenvector centrality with centrality_eigen() function (no parameter) select 25 nodes with highest eigenvector centarlity value visualize the network using: ggraph() function with "kk" as the layout geom_edge_fan() geom_node_point() geom_node_text -> only show nodes label with eigenvector centarlity value >= 1. use parameter repel=T theme_graph()

1. from your plot output, select the most similar picture below
• [ ] a assets/opt_a.PNG
• [ ] b assets/opt_b.PNG
• [ ] c assets/opt_c.PNG)
1. Imagine this is a terrorist communication network. They use small community (also called as subgraph) to communicate to avoid suspicion. We are told to find which node is the most important for spreading information between subgraph. Without using any filter and communtiy, calculate eigenvector, betweenness, closeness, degree centrality and select which pair of nodes that need to be terminated first.
• [ ] 66, 104
• [ ] 208, 46
• [ ] 172, 165
• [ ] 46, 28
Quiz
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You have 1 attempt. Only your highest score will be taken into account.
• ###### Quiz 1

Which statement is TRUE about graph theory & SNA: (a) Network can be divided into 2 types: directed and undirected. (b) SNA is basicly an implementation of graph theory in human social life. (c) In-degree in directed network, is a two-way relationship between nodes. usually drawn with lines without arrows. (d) Social Network Analysis can be implemented in wide areas, including finding information networks within terrorist group.

Question worth 1 point

• ###### Quiz 2

From the picture below. Calculate the shortest path between nodes 4 to nodes 13

Question worth 1 point

• ###### Quiz 3

• Closeness centrality calculate shortest path from specific nodes to every nodes in the network. Thus the more central a node is, the closer it is to all other nodes. From the statements below, choose the correct intepretation of closeness centrality.
• Question worth 1 point

• ###### Quiz 4

• Select the correct statements based on your output
• Question worth 1 point

• ###### Quiz 5

• Calculate betweenness, closeness, degree centrality and select which node have the highest centrality value consecutively
• Question worth 1 point

• ###### Quiz 6

• from your plot output, select the most similar picture below
• Question worth 1 point

• ###### Quiz 7

• Imagine this is a terrorist communication network. They use small community (also called as subgraph) to communicate to avoid suspicion. We are told to find which node is the most important for spreading information between subgraph. Without using any filter and communtiy, calculate eigenvector, betweenness, closeness, degree centrality and select which pair of nodes that need to be terminated first.
• Question worth 1 point

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