In this exercise, we will learn how to create a network object from an adjacency matrix using the statnet package in R. This is a fundamental step in network analysis as it allows us to convert raw data into a format that can be used for further analysis.
First, we need to generate some data in matrix format. This matrix will represent our network, where each row and column corresponds to a node, and the value at the intersection of a row and column indicates whether an edge exists between those nodes.
Here’s an example of how to generate such a matrix:
adjacency_matrix <- rbind(
  c(0,1,1,0,0),
  c(0,0,1,1,0),
  c(0,1,0,0,0),
  c(0,0,0,0,0),
  c(0,0,1,0,0)
)
Next, we provide names for the rows and columns. These names correspond to the nodes in our network:
rownames(adjacency_matrix) <- c("A", "B", "C", "D", "E")
colnames(adjacency_matrix) <- c("A", "B", "C", "D", "E")
Now that we have our matrix, we can create a network object from it using the network() function. 
Since we’re using an adjacency matrix as input, we specify this with the matrix.type parameter:
adjacency_network <- network(adjacency_matrix, matrix.type="adjacency")
To confirm that our network object was created successfully, we can check its class type with the class() function:
class(adjacency_network)
[1] "network"
We can also use the summary() function to get more information about the network, such as the number of vertices:
summary(adjacency_network)
Network attributes:
  vertices = 5
  directed = TRUE
  hyper = FALSE
  loops = FALSE
  multiple = FALSE
  bipartite = FALSE
 total edges = 6 
   missing edges = 0 
   non-missing edges = 6 
 density = 0.3 
Vertex attributes:
  vertex.names:
   character valued attribute
   5 valid vertex names
No edge attributes
Network adjacency matrix:
  A B C D E
A 0 1 1 0 0
B 0 0 1 1 0
C 0 1 0 0 0
D 0 0 0 0 0
E 0 0 1 0 0
Finally, we can visualize our network using the gplot() function:
gplot(adjacency_network, vertex.col = 2, displaylaels = TRUE)

        The adjacency matrix below defines whether Adam, Bob, Carol, Diesel and Erika are friends. 
        Give the adjacency matrix appropriate row and column names and 
        create a network object called friends_network.
    
Assume that: