64,953 research outputs found
Extraction and Analysis of Facebook Friendship Relations
Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud
However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms
Measuring social dynamics in a massive multiplayer online game
Quantification of human group-behavior has so far defied an empirical,
falsifiable approach. This is due to tremendous difficulties in data
acquisition of social systems. Massive multiplayer online games (MMOG) provide
a fascinating new way of observing hundreds of thousands of simultaneously
socially interacting individuals engaged in virtual economic activities. We
have compiled a data set consisting of practically all actions of all players
over a period of three years from a MMOG played by 300,000 people. This
large-scale data set of a socio-economic unit contains all social and economic
data from a single and coherent source. Players have to generate a virtual
income through economic activities to `survive' and are typically engaged in a
multitude of social activities offered within the game. Our analysis of
high-frequency log files focuses on three types of social networks, and tests a
series of social-dynamics hypotheses. In particular we study the structure and
dynamics of friend-, enemy- and communication networks. We find striking
differences in topological structure between positive (friend) and negative
(enemy) tie networks. All networks confirm the recently observed phenomenon of
network densification. We propose two approximate social laws in communication
networks, the first expressing betweenness centrality as the inverse square of
the overlap, the second relating communication strength to the cube of the
overlap. These empirical laws provide strong quantitative evidence for the Weak
ties hypothesis of Granovetter. Further, the analysis of triad significance
profiles validates well-established assertions from social balance theory. We
find overrepresentation (underrepresentation) of complete (incomplete) triads
in networks of positive ties, and vice versa for networks of negative ties...Comment: 23 pages 19 figure
Finding influential spreaders from human activity beyond network location
Most centralities proposed for identifying influential spreaders on social
networks to either spread a message or to stop an epidemic require the full
topological information of the network on which spreading occurs. In practice,
however, collecting all connections between agents in social networks can be
hardly achieved. As a result, such metrics could be difficult to apply to real
social networks. Consequently, a new approach for identifying influential
people without the explicit network information is demanded in order to provide
an efficient immunization or spreading strategy, in a practical sense. In this
study, we seek a possible way for finding influential spreaders by using the
social mechanisms of how social connections are formed in real networks. We
find that a reliable immunization scheme can be achieved by asking people how
they interact with each other. From these surveys we find that the
probabilistic tendency to connect to a hub has the strongest predictive power
for influential spreaders among tested social mechanisms. Our observation also
suggests that people who connect different communities is more likely to be an
influential spreader when a network has a strong modular structure. Our finding
implies that not only the effect of network location but also the behavior of
individuals is important to design optimal immunization or spreading schemes
A Relational Hyperlink Analysis of an Online Social Movement
In this paper we propose relational hyperlink analysis (RHA) as a distinct approach for empirical social science research into hyperlink networks on the World Wide Web. We demonstrate this approach, which employs the ideas and techniques of social network analysis (in particular, exponential random graph modeling), in a study of the hyperlinking behaviors of Australian asylum advocacy groups. We show that compared with the commonly-used hyperlink counts regression approach, relational hyperlink analysis can lead to fundamentally different conclusions about the social processes underpinning hyperlinking behavior. In particular, in trying to understand why social ties are formed, counts regressions may over-estimate the role of actor attributes in the formation of hyperlinks when endogenous, purely structural network effects are not taken into account. Our analysis involves an innovative joint use of two software programs: VOSON, for the automated retrieval and processing of considerable quantities of hyperlink data, and LPNet, for the statistical modeling of social network data. Together, VOSON and LPNet enable new and unique research into social networks in the online world, and our paper highlights the importance of complementary research tools for social science research into the web
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