Friend Cloud: Identifying Networks and Connections Between Facebook Friends. Ben Fuja and Joey Rich. Programming Paradigms May 12, 2011

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1 Friend Cloud: Identifying Networks and Connections Between Facebook Friends Ben Fuja and Joey Rich Programming Paradigms May 12, Abstract Facebook has become a ubiquitous aspect of the social world, and the importance of one s personal life online is quickly catching up with the importance of real world interactions. With such a trend, it is important to have the online sphere mimic as closely as possible the dynamics that exist offline. Friends online can cover a wide range of relationships, but the manual formation of groups that Facebook provides is ineffective and not practical for individuals with hundreds or even thousands of friends. Family, coworkers, and acquaintances are all designated as friends through Facebook, but this umbrella does not address the unique characteristics that separate these networks. The idea for this project was formulated to address this discrepancy between the online and offline social experiences. Through information available through Facebook, the Friend Cloud application effectively identifies these separate networks that share the title of friend. Capabilities were also included to display and manage these networks, so multiple groups could be browsed in an interactive manner and even merged into a single network. The success of this project was evidenced by not only the separation of various friends, but also the accuracy with which these networks reflected real-world relationships and connections. 2 Key Words Facebook: Online social community that provides a forum for friends and associates to interact online. Friend: The term friend in this context is used as a connection between two people on Facebook. Family and coworkers fall under the abstract definition of friend that Facebook has established. Mutual Friend: A Facebook user who connects the social graph of two other Facebook users. Mutual friends are used in our algorithm to decide membership of separate networks.

2 Network: Group of friends that share a common link. Networks in this implementation are created through a common friend amongst all members of the network 3 Introduction The social experience in today s society has shifted to the online theatre. Social networking sites such as Facebook have defied the downfalls of a fad and are instead an established aspect of society that will evolve, as opposed to die out, over coming generations. The wealth of information that is stored on Facebook provides a platform for the development of data-intensive applications. With the permission of a Facebook user, programmers can be granted access to this information and use the groups, communications, and connections to better analyze and sort through the social graph that is at the core of Facebook. Although these connections between friends are readily available to the minds at Facebook, the formation of groups and privacy settings still relies mainly on manual sorting. If a system were implemented by which networks were automatically generated, news and settings could be applied differently in a more user-friendly manner. This project makes use of the huge amounts of data from Facebook to construct appropriate networks amongst your online friends. Over the course of our project, applications such as TouchGraph (touchgraph.com/facebook) and SocialGraph (apps.facebook.com/socgraph) were found to be current projects with the same initiative and purpose as our own. These programs, however, focused more on the connections, themselves, as opposed to the distinct groups that these connections create, which was the original intention of our application. Figure 1: TouchGraph s modeling of Facebook connections [1]

3 4 Usage examples When a user accesses friend-cloud.com, he/she will be greeted by the main screen along with a message prompting the user to log in to their Facebook account. Upon clicking the login button, a Facebook dialog will pop up for the user to enter their Facebook and password. After this dialog, the user will need to grant permission to allow Friend Cloud to access their Facebook profile and information. Figure 2: Dialog to sign in to Facebook account After the user is logged into their Facebook account and granted the proper permissions, Friend Cloud will begin to download the required data from the site. A status bar shows the current progress of the downloading process. It is at this time that the information on friendships is stored into the Google App Engine database. Figure 3: A status bar shows the progress of Friend Cloud s data retrieval and storage

4 Once the retrieval of Facebook data is complete, the user continues to follow the flow of steps outlined on the site. A link to Build my Friend Networks assigns the user s friends into networks, information that is subsequently stored in the database. These networks are then viewed in list format, with the name of the network being displayed alongside the number of friends who are belong to it. After this step, the user has the opportunity to View my Friend Networks by clicking a link provided. This is the final step that creates an interactive canvas for the browsing and merging of networks. The center of the network is a friend of the user with whom all other members of the network are mutual friends. This friend s profile picture is displayed in the top left of a frame that contains the profile pictures of all other friends in the network. Hovering the cursor over a friend s profile picture brings up the friend s name, profile picture, number of mutual friends, as well as a dropdown list of networks to which this friend can be reassigned. This tool is implemented with JavaScript without a page refresh, while the Merge Network feature deletes one network, adds its members to another, and refreshes the new network section of the page. Figure 4: The final screen allows the user to view his/her networks

5 5 Approach and Methods This project was developed in Python and made extensive use of the Google App Engine. Google App Engine provides several useful mechanisms for the creation and maintenance of an online application. Our page made most use of the available database and distributed computational capabilities that Google App Engine offers. These different features can be accessed through the Google App Engine development site, appengine.google.com. Google enforces daily quotas of CPU time, database access, and many other metrics, which could pose limitations, were this initiative to be expanded. The database used in this case holds onto user data such as networks and friendships, so a user can access the site multiple times with no need to regenerate statistics for his/her Facebook profile. The website also relied heavily on the information made available through the Facebook Application Programming Interface (API). The Facebook API allows for programs to make use of the data that Facebook users volunteer for their personal profiles. By signing in through Facebook and password, users can allow these programs to access information such as friends, pictures, wall posts, and other parts of Facebook. The current version of the website only makes use of friendships and profile pictures, but there exists the capability for many more additions with the features of the Facebook API. The algorithm for organizing networks was designed to balance the strength of connections between members but also limit computational complexity, since a faster execution leads to a better user experience as well as less share of Google App Engine s daily quotas. The final version of this algorithm worked to establish which friend a user shared the most mutual friends with. This friend was then considered the head of a network that included all of the mutual friends shared with the main user. All members of this network are then removed from a list of friends unassigned to a network. This process is repeated with this list of unassigned friends, so the second network was built around the remaining friend who has shares the most common friends with the main user and members of that network are removed from the unassigned list. This process is repeated for all networks that have at least four members (including the friend around whom the network is centered). After this process is completed, all remaining friends are put into an Uncategorized network, but can still be moved to other networks at the will of the user.

6 6 Discussion and Future Work The current implementation of Friend Cloud does a reasonably good job of identifying subnetworks among a user s Facebook friends, but there are several ways that our group identification algorithm could be improved, and there is also room for improvement in the user interface for managing these groups. One possible improvement to the group identification algorithm would be to re-calculate the mutual friend tally after a group is identified and removed. This re-calculated mutual friend tally would exclude mutual friendships in which the mutual friend is a member of an existing group. This way, the algorithm would choose the person with the highest mutual friend tally of the remaining friends to be the leader of the next group. This algorithm would do a better job of covering the network with a minimal number of groups than the current algorithm, which only approximates this behavior. Recalculating the mutual friend tally can be implemented in two ways, both of which are computationally expensive. One way would be to do a nested for loop which would compare each of the remaining ungrouped friends to every other remaining ungrouped friend, after each group-selection iteration. This all-pairs operation is expensive, but not impossibly expensive since we have a local database record for every friendship, so at least we avoid any network latency which would be incurred if we were actively making API calls to Facebook. The other, slightly better method is to decrement mutual friend tallys as friends are removed from the active pool and are placed into their subnetworks. In this method, as we put user A into a group, we would loop through all friendships of user A and decrement the friend tally of any of user A s friends who were still in the active pool. Another heuristic for improving group selection would be refining group memberships after the basic network groupings have been completed. Currently, we have a simple refining step where groups which contain only 1 or 2 members are lumped together into an Uncategorized group. Since this Uncategorized group is small compared to the the size of all of the user s Facebook friends, we can spend extra computation on these members without adding much overall time to the group identification step. One technique that we could use would be to loop through all of the mutual friends of each of these uncategorized members and add this uncategorized member to the group which has the most hits. Similarly, mutual friend analysis by group could be carried out on people who are already members of existing groups, to see if their current membership is really the best choice. For example, it is possible that one of

7 my college friends has been drawn into my high school friends group because of a single mutual friendship with one of my high school friends, even though this person has a much greater number of mutual friends who are in my college friends group. These errors could be corrected in this refinement analysis stage. Besides these improvements to our grouping algorithm, we would also like to add more features to our group management interface. One obvious feature would be to allow people to be members of multiple different networks. For example, someone might be a member of my college friends network and also my high school friends network. Another important feature which we have not yet implemented is allowing users to create new groups. This could be useful for users who want a greater level of customization over their groups, instead of just using the groups which have been identified by our algorithm. 7 References [1] TouchGraph: Graph Connections. Provided by touchgraph.com

8 8 Authors Joey Rich is a Computer Science major at Notre Dame, originally from North Manchester Indiana. Joey enjoys playing sports and travelling the world, and is passionate about web development and internet entrepreneurship. Ben Fuja is a Computer Science major at Notre Dame. Ben has lived in the greater South Bend area since 1998 and has an older sister who graduated from Notre Dame in Not surprisingly, he is a fan of Notre Dame sports as well as the Chicago Cubs in baseball and Chicago Bulls in basketball. Ben hopes to use his programming knowledge in the future with a career in technical consulting.