The above diagram demonstrates the relationship between network nodes and edges. These are two different species. The nodes and edges represent different things for different types of network. For example, for the social network, each node represent the a person and the edges represent the relation between nodes and edges. If it is citation network, then nodes represent the papers and the edges represent citations.
Besides, Dr. Ram also talked about the degree centrality, which is defined as the number of links incident upon a node. The term "closeness" is regarded as a measure of how long it will take to spread information from one node to all the other nodes sequentially. Betweenness centrality can be a measure for quantifying the control of a human on the communication between other humans in a social network. Eigenvector centrality is the measure of the influence of a node in a network, which can be assigned scores to all nodes based on the concept that connections to high-scoring nodes contribute more to the score of the node than equal connections to low-scoring nodes.
A clustering coefficient is a measure of degree to which nodes in a graph tend to cluster together. in most real-world social networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties.
The adjacency matrix is a matrix with rows and columns labeled by graph vertices. The rows and columns can be all the nodes. If two nodes have connections, then we can put "1" in it, otherwise put a "0".
In additional to all the knowledge mentioned above, Dr. Ram also introduced the Gephi software which makes a graph that shows how we connect with other people.
This whole concepts are all about the networks between people, which give us a basic information on how to collect and analyze data to make data useful.
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