19,485 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
Extended Edgecluster based Technique for Social Networking Collective Behavior Learning System
Growing interest and continuous development of social network sites like Facebook Twitter Flicker and YouTube etc turn to several researchers for research study planning and rigorous development Exact people behavior prediction is the most important challenge of these on-line social networking websites This research focus to learn to predict collective behavior in social media networks Particularly provided information about some person how can we collect the behavior of unobserved persons in the same network These tremendous growing networks in social media are of massive size involving large number of actors The computational scale of these networks makes necessary scalable learning for models for collective collaborative behavior prediction This scalability issue is solved by the proposed k-means clustering algorithm which is used to partition the edges into disjoint distinct sets with each set is showing one separate affiliation This edge-centric structure represents that the extracted social dimensions are definitely sparse in nature This model idealized on the sparse natured social dimensions shows efficient prediction performance than earlier existing approaches The proposed approach can effectively able to work for sparse social networks of any growing size The important advantage of this method is that it easily grows upon to handle networks with large number of actors while existing methods was unable to do This scalable approach effectively used over of online network collective behavior on a large scal
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
Future work selves : how salient hoped-for identities motivate proactive career behaviors
The term future work self refers to an individual's representation of himself or herself in the future that reflects his or her hopes and aspirations in relation to work. The clearer and more accessible this representation, the more salient the future work self. An initial study with 2 samples (N = 397; N = 103) showed that future work self salience was distinct from established career concepts and positively related to individuals' proactive career behavior. A follow-up longitudinal analysis, Study 2 (N = 53), demonstrated that future work self salience had a lagged effect on proactive career behavior. In Study 3 (N = 233), we considered the role of elaboration, a further attribute of a future work self, and showed that elaboration motivated proactive career behavior only when future work self salience was also high. Together the studies suggest the power of future work selves as a motivational resource for proactive career behavior. (PsycINFO Database Record (c) 2012 APA, all rights reserved
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