Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks. This paper presents a large-scale measurement study and analysis of the structure of multiple online social networks. We examine data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut. We crawled the publicly accessible user links on each site, obtaining a large portion of each social network’s graph. Our data set contains over 11.3 million users and 328 million links. We believe that this is the first study to examine multiple online social networks at scale. Our results confirm the power-law, small-world, and scalefree properties of online social networks. We observe that the indegree of user nodes tends to match the outdegree; that the networks contain a densely connected core of high-degree nodes; and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network. Finally, we discuss the implications of these structural properties for the design of social network based systems.

Word-of-mouth communication has been shown to play a key role in a variety of environments such as viral marketing and virus spreading. A family of algorithms, generally known as information spreading algorithms or word-of-mouth algorithms, has been developed to characterize such behavior. However, they have limitations, including the inability to: (1) capture when the communications or contacts take place and (2) explain where the influence comes from. These drawbacks have limited the studies about how the spreading of influence takes place in social networks. In this paper, we present a new word-of-mouth algorithm that considers the temporality of the communications and keeps track of how influence travels over the social network. We validate the proposed algorithm via simulations of word-of-mouth traces on call detailed records, in order to model how influence spreads. Our results indicate that (1) static factors of social networks are not enough to model influence and (2) there seems to be statistical invariants of how influence spreads in a network. 1

Word-of-Mouth algorithms: What you don’t know will hurt you Word-of-mouth communications has been shown to play a key role in a variety of environments such as viral marketing and churn prediction. A family of algorithms, generally known as information spreading algorithms has been developed to model such pervasive behavior. Although these algorithms have produced good results, in general, they do not consider that the social network reconstructed to model the environment of an individual is limited by the information available. In this paper we study how the missing information (in the form of missing nodes and/or missing links) affects the spread of information in the well-known Dasgupta et al. (2008) algorithm. The results indicate that the error made grows logarithmically with the amount of information (links, nodes or both) unknown. 1

Google Buzz is a novel online service that presents new opportunities for social network analysis. By initializing the Buzz network with existing Gmail contacts, Google provides a unique dataset that may reflect a different aspect of online communication from those found in existing networks such as Facebook and Twitter. In this paper we design heuristic metrics for ranking and recommending influential members of the Buzz social network. We leverage these metrics to develop an application allowing individual Buzz users to identify influential users near their existing “friend ” subgraph.