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.

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Traditional online social network sites use a single monolithic “friends ” relationship to link users. However, users may have more in common with strangers, suggesting the use of a “similarity network ” to recommend content. This paper examines the usefulness of this distinction in propagating new content. Using both macroscopic and microscopic social dynamics, we present an analysis of Essembly, an ideological social network that semantically distinguishes between friends and ideological allies and nemeses. Although users have greater similarity with their allies than their friends and nemeses, surprisingly, the allies network does not affect voting behavior, despite being as large as the friends network. In contrast, users are influenced differently by their friends and nemeses, indicating that people use these networks for distinct purposes. We suggest resulting design implications for social content aggregation services and recommender systems. Author Keywords

Online social networking is increasingly popular and is a feature of many websites. Providing multiple types of relationship, such as friend, fan or colleague, can enhance the significance of the networks. We present an empirical study of an online political forum where users engage in content creation, voting, and discussion. The users also make explicit connections via three relationship types, forming three distinct networks. We establish a strong correlation between network participation and site activity and show that users stayed faithful to the relationship semantics, in aggregate. Moreover, we demonstrate significant structural differences among the networks, indicating different uses for each. Social networking features may help spread the word about new content, but we show that the networks played a surprisingly moderate role in this respect. Usage and social networking patterns were typical of many web communities, suggesting that multiple relationship types could be successfully featured in other such communities.

This paper demonstrates that "social network collaborative filtering " (SNCF), wherein user-selected like-minded alters are used to make predictions, can rival traditional user-to-user collaborative filtering (CF) in predictive accuracy. Using a unique data set from an online community where users rated items and also created social networking links specifically intended to represent likeminded “allies, ” we use SNCF and traditional CF to predict ratings by networked users. We find that SNCF using generic "friend " alters is moderately worse than the better CF techniques, but outperforms benchmarks such as byitem or by-user average rating; generic friends often are not like-minded. However, SNCF using "ally " alters is competitive with CF. These results are significant because SNCF is tremendously more computationally efficient than traditional user-user CF and may be implemented in large-scale web commerce and social networking communities. It is notoriously difficult to distinguish the contributions of social influence (where allies influence users) and "social” selection (where users are simply effective at selecting like-minded people as their allies). Nonetheless, comparing similarity over time, we do show no evidence of strong social influence among allies or friends. Categories and Subject Descriptors:

Nowadays social websites have become a major trend in the Web 2.0 environment, enabling abundant social data available. In this paper, we explore the role of two types of social relationships: membership and friendship, while being fused with traditional CF (Collaborative Filtering) recommender methods in order to more accurately predict users ’ interests and produce recommendations to them. Through an exploratory evaluation with real-life dataset from Last.fm, we have revealed respective effects of the two explicit relationships and furthermore their combinative impacts. In addition, the fusion is conducted via random walk graph model in comparison with via weighted neighborhood similarity matrix, so as to identify the best performance platform. Indepth analysis on the experimental data particularly shows the significant improvement by up to 8 % on recommendation accuracy, by embedding social relationships in CF via graph model.

From its beginnings in the 1960ies and 1970ies, the Internet has been used for communication and collaborative work. While early adopters mostly worked within academia and the military, its rapid diffusion within the last 15 years has broadened the user base massively. Nowadays, the Internet is a widely used technological

Social networking sites such as MySpace and Facebook thrive on energetic social interaction, but the factors that assure this are not well understood. There is a lack of theory that can describe and predict the successful adoption of new social computing systems. This paper introduces the Social Software Performance Model, and uses it to interpret the evolution and usage of social networking sites. Drawing from socio-technical systems theory, task technology fit, and structuration theory, this model identifies the components of social software, and describes their role in the evaluation and adoption of these systems. The results of three studies are presented, providing initial empirical evidence for the model.

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