On composing my network graph for the lab assignment I gained a clear understanding of the network terms we had discussed in class as well as familiarizing myself with the Gephi software. Gephi is a type of software that visualises network data. To begin with I found the process of creating the graph a bit challenging as I found the software itself a bit confusing but once I got comfortable with the software and understood more of what I had to achieve, it eventually all came together. It was very interesting to see the connections amongst my friends and the degree of closeness within the clusters. On completing the graph I aimed to follow Schneiderman's ‘Netvizz Nirvana' standard of graph clarity. On doing so I made sure that every vertex was visible, every vertex’s degree was countable, every edge could be followed from source to destination and all clusters and outliers were identifiable.  There are a lot of steps to creating your graph with Gephi. After you have installed everything you must import your Facebook network. Originally you start off with a huge blob full of nodes and edges and it’s not very informative.  The nodes represent the Facebook friends and edges represent to whom they are connected.  By choosing a layout I was then able to see the small communities within my network of friends and by doing so it was clear that there was some very connected edges meaning those people knew each other very well. I decided to go with the layout ‘forced atlas’ to make it more informative. Once you decide on your layout, you then press run and stop when you are happy with the layout. You will then clearly see your communities which illustrate your communities from your network of friends. To make it more comprehensible I zoomed in and focused on three clusters of my friends. By pressing the T button on the bottom left hand side of the screen you can add labels to the nodes to see exactly who the nodes represent.  I was then able to see who exactly was in the three clusters that I had focused on. It was very interesting to see who was connected and through who they were connected. There were a few friends of mine that I didn’t even know they knew each other!  I then decided to colour code the graph to make it more appealing and understandable. To do this I ran modularity under the statistics tab. This then devised what community have a lot of modularity and what community has little modularity.  Another useful tip is to know what friend knows all your friends. In my case, this was very obvious, Andrew Barry. But in many of my other graphs it was not so obvious. To do this you must go into average degree in the statistics tab and press run. It will calculate for each node how many degrees go inside of them or outside of them. By choosing a rank parameter, either in degree or out degree and setting the minimum and maximum size, you will then see the result.  The only problem that did arise was when I exported the graph into a pdf it kept cutting off the edges of the graph which lead me to move the clusters closer together. This seemed to be my only alternative and therefore none of the labels got cut off although the graph itself looked very close together and not as appealing as I would have liked it to be.  In completing this graph I grasped the term’ eign-vector centrality’ by visually understanding how well connected  are my friends that are connected to me. It was very interesting to see the three communities of my directed relationships and how they were connected.  They are three groups of friends that I have in college, one group is friends from my Irish class, another cluster is friends from a Gaeltaucht summer course that I did and the third cluster is a group of friends I have through a friend, Andrew Barry. It is clear from the graph that Andrew Barry has strong interpersonal ties with the three communities as there is direct bridging from each community towards him.  Overall, I had a great experience getting to know this software and learning the new terms that helped me understand the layout of the graph.