Graph Report   In this report, I will discuss how I went about creating my Gephi Graph, and will then discuss the network, and the connections of the graph.  Creating the Graph  To create the graph, I used a tool called Gephi. Gephi is an open source network analysis and visualization software application, available for free download online. Gephi allows the user to work with complex data and produce valuable, attractive visual results.   I used my personal Facebook network as the data to input into Gephi. To do this I used “Netvizz”. Netvizz is an online Facebook application that allows you to create a GDF file, which is compatible with Gephi, of all your Facebook connections. After retrieving the GDF file, I saved it to my computer and opened Gephi.   When the GDF file is inputted initially to Gephi, the data is all clumped together in a concentrated area that resembles something like an intricate rubber band ball. To make sense of the connections, I clicked into the Layout module and selected ‘Force Atlas’ from the dropdown menu, then clicked RUN. Force Atlas makes the connected nodes attached to each other, and pushes the unconnected nodes apart, which results in clusters of connections, which makes it easy for the user to see connections within the network. The nodes on the graph represent a Facebook friend of mine/group member. The edge (line) between each node represents the Facebook connection/friendship of that person with my other friends and group members.   I then clicked on the “Ranking” tab and chose “Degree” from the menu. This option shows the number of edges within the network through colour. I clicked APPLY and the graph changed colour accordingly. Another way of colouring the nodes by community connection is by selecting the “Brush” tool located beside the graph. I hovered over a node with this option selected and saw how many other nodes that one particular node is connected with. I used this option for the majority of my node colouring as I found it more effective visually.   My network was very large with almost 600 connections, so to reduce the number of connections to make the nodes and edges more visible I reduced the number of nodes shown, using the Filters tab, then selecting Topology, then I adjusted the degree range. The clusters remained the same, but just had fewer nodes within, so that the graph could be more easily read. I clicked the black bold T at the bottom of the toolbar to turn on the labels of the nodes (my friends names.) I reduced the size of the labels to reduce the appearance of clutter.   At this point my graph was pretty much complete, I had four distinct clusters that represent the four main communities of my life: 1) my family 2) my school friends 3) my college friends, 4) my team mates. Each cluster had a different colour and, as result, stand out clearly on the graph. However Gephi’s statistics and metrics framework offer the most common metrics for social network analysis, so I played around with a few of the options.   I wanted to test the four different centralities on my network. Centrality refers to indicators that identify the most important vertices/group within a graph. I had already conducted degree centrality on my graph, so I began to measure the betweenness centrality of my network, which is a process that measures the number of times a node acts as a bridge along the shortest path between two other nodes. Closeness centrality refers to the spread of information by the use of shortest paths. Eigenvector centrality measures the influence of a node in a network.  Out of all four centralities, I found degree and eigenvector centrality to be more useful and relevant to my network.   Brief Conclusion of My Gephi Graph  My graph consists of 4 clusters, which represent the four communities in my life. One thing I noticed is that my network is very closely linked, even though there are 4 distinct clusters. There are at least two if not more nodes in each cluster that are directly linked with another cluster.   This graph representation shows that my network has strong interpersonal ties.