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239
De-anonymizing social networks
, 2009
"... Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc. We present ..."
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Cited by 216 (6 self)
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Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc. We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized socialnetwork graphs. To demonstrate its effectiveness on realworld networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12 % error rate. Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the adversary’s auxiliary information is small.
Signed networks in social media
- In Proc. 28th CHI
, 2010
"... Relations between users on social media sites often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. In contrast to the bulk of research on social networks that has focused almost exclusively on positive interpretations of links between people, we study how the inte ..."
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Cited by 113 (9 self)
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Relations between users on social media sites often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. In contrast to the bulk of research on social networks that has focused almost exclusively on positive interpretations of links between people, we study how the interplay between positive and negative relationships affects the structure of on-line social networks. We connect our analyses to theories of signed networks from social psychology. We find that the classical theory of structural balance tends to capture certain common patterns of interaction, but that it is also at odds with some of the fundamental phenomena we observe — particularly related to the evolving, directed nature of these on-line networks. We then develop an alternate theory of status that better explains the observed edge signs and provides insights into the underlying social mechanisms. Our work provides one of the first large-scale evaluations of theories of signed networks using on-line datasets, as well as providing a perspective for reasoning about social media sites. Author Keywords signed networks, structural balance, status theory, positive
The Structure of Information Pathways in a Social Communication Network
"... Social networks are of interest to researchers in part because they are thought to mediate the flow of information in communities and organizations. Here we study the temporal dynamics of communication using on-line data, including e-mail communication among the faculty and staff of a large universi ..."
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Cited by 105 (2 self)
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Social networks are of interest to researchers in part because they are thought to mediate the flow of information in communities and organizations. Here we study the temporal dynamics of communication using on-line data, including e-mail communication among the faculty and staff of a large university over a two-year period. We formulate a temporal notion of “distance ” in the underlying social network by measuring the minimum time required for information to spread from one node to another — a concept that draws on the notion of vector-clocks from the study of distributed computing systems. We find that such temporal measures provide structural insights that are not apparent from analyses of the pure social network topology. In particular, we define the network backbone to be the subgraph consisting of edges on which information has the potential to flow the quickest. We find that the backbone is a sparse graph with a concentration of both highly embedded edges and long-range bridges — a finding that sheds new light on the relationship between tie strength and connectivity in social networks.
The Role of Social Networks in Information Diffusion
, 2012
"... Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would sti ..."
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Cited by 87 (3 self)
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Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends ’ information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.
Human mobility, social ties, and link prediction
- In KDD
, 2011
"... Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evo-lution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simu ..."
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Cited by 63 (5 self)
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Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evo-lution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simultaneously capture the dynamical information on indi-vidual movements and social interactions. Here we address this challenge for the first time by tracking the trajecto-ries and communication records of 6 Million mobile phone users. We find that the similarity between two individuals’ movements strongly correlates with their proximity in the social network. We further investigate how the predictive power hidden in such correlations can be exploited to ad-dress a challenging problem: which new links will develop in a social network. We show that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures. Furthermore, the prediction accu-racy can be significantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link pre-diction but also network dynamics.
Critical Questions for Big Data: Provocations for a Cultural
- Technological, and Scholarly Phenomenon.”Information, Communication, & Society
, 2012
"... Technology is neither good nor bad; nor is it neutral...technology’s interaction with the social ecology is such that technical developments frequently have environmental, social, and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves. Melv ..."
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Cited by 48 (0 self)
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Technology is neither good nor bad; nor is it neutral...technology’s interaction with the social ecology is such that technical developments frequently have environmental, social, and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves. Melvin Kranzberg (1986, p. 545) We need to open a discourse⎯where there is no effective discourse now⎯about the varying temporalities, spatialities and materialities that we might represent in our databases, with a view to designing for maximum flexibility and allowing as possible for an emergent polyphony and polychrony. Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care.
Networks formed from interdependent networks
- PHYS
, 2011
"... ... obtained by analysing isolated networks, many real-world networks do in fact interact with and depend on other networks. The set of extensive results for the limiting case of non-interacting networks holds only to the extent that ignoring the presence of other networks can be justified. Recently ..."
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Cited by 45 (6 self)
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... obtained by analysing isolated networks, many real-world networks do in fact interact with and depend on other networks. The set of extensive results for the limiting case of non-interacting networks holds only to the extent that ignoring the presence of other networks can be justified. Recently, an analytical framework for studying the percolation properties of interacting networks has been developed. Here we review this framework and the results obtained so far for connectivity properties of ‘networks of networks’ formed by interdependent random networks.
Social Structure of Facebook Networks
, 2011
"... We study the social structure of Facebook “friendship ” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes—gender, class year, major, high school, and residence—at these institutions. We investigate the influence of commo ..."
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Cited by 43 (2 self)
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We study the social structure of Facebook “friendship ” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes—gender, class year, major, high school, and residence—at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on the user characteristics. We thereby compare the relative importances of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.
Comparing community structure to characteristics in online collegiate social networks
- SIAM Review
, 2011
"... Abstract. We study the structure of social networks of students by examining the graphs of Facebook “friendships ” at five U.S. universities at a single point in time. We investigate the community structure of each single-institution network and employ visual and quantitative tools, including standa ..."
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Cited by 42 (5 self)
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Abstract. We study the structure of social networks of students by examining the graphs of Facebook “friendships ” at five U.S. universities at a single point in time. We investigate the community structure of each single-institution network and employ visual and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the basic properties and statistics of the employed pair-counting indices and recall, in simplified notation, a useful formula for the z-score of the Rand coefficient. Our study illustrates how to examine different instances of social networks constructed in similar environments, emphasizes the array of social forces that combine to form “communities, ” and leads to comparative observations about online social structures, which reflect offline social structures. We calculate the relative contributions of different characteristics to the community structure of individual universities and compare these relative contributions at different universities. For example, we examine the importance of common high school affiliation at large state universities and the varying degrees of influence that common major can have on the social structure at different universities.
