90,701 research outputs found
IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis
We conduct the most comprehensive study of WLAN traces to date. Measurements
collected from four major university campuses are analyzed with the aim of
developing fundamental understanding of realistic user behavior in wireless
networks. Both individual user and inter-node (group) behaviors are
investigated and two classes of metrics are devised to capture the underlying
structure of such behaviors.
For individual user behavior we observe distinct patterns in which most users
are 'on' for a small fraction of the time, the number of access points visited
is very small and the overall on-line user mobility is quite low. We clearly
identify categories of heavy and light users. In general, users exhibit high
degree of similarity over days and weeks.
For group behavior, we define metrics for encounter patterns and friendship.
Surprisingly, we find that a user, on average, encounters less than 6% of the
network user population within a month, and that encounter and friendship
relations are highly asymmetric. We establish that number of encounters follows
a biPareto distribution, while friendship indexes follow an exponential
distribution. We capture the encounter graph using a small world model, the
characteristics of which reach steady state after only one day.
We hope for our study to have a great impact on realistic modeling of network
usage and mobility patterns in wireless networks.Comment: 16 pages, 31 figure
Maximum likelihood estimation for social network dynamics
A model for network panel data is discussed, based on the assumption that the
observed data are discrete observations of a continuous-time Markov process on
the space of all directed graphs on a given node set, in which changes in tie
variables are independent conditional on the current graph. The model for tie
changes is parametric and designed for applications to social network analysis,
where the network dynamics can be interpreted as being generated by choices
made by the social actors represented by the nodes of the graph. An algorithm
for calculating the Maximum Likelihood estimator is presented, based on data
augmentation and stochastic approximation. An application to an evolving
friendship network is given and a small simulation study is presented which
suggests that for small data sets the Maximum Likelihood estimator is more
efficient than the earlier proposed Method of Moments estimator.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS313 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Towards inferring communication patterns in online social networks
Grup de recerca: Security of Networks and Distributed Applications (SENDA)The separation between the public and private spheres on online social networks is known to be, at best, blurred. On the one hand, previous studies have shown how it is possible to infer private attributes from publicly available data. On the other hand, no distinction exists between public and private data when we consider the ability of the online social network (OSN) provider to access them. Even when OSN users go to great lengths to protect their privacy, such as by using encryption or communication obfuscation, correlations between data may render these solutions useless. In this article, we study the relationship between private communication patterns and publicly available OSN data. Such a relationship informs both privacy-invasive inferences as well as OSN communication modelling, the latter being key toward developing effective obfuscation tools. We propose an inference model based on Bayesian analysis and evaluate, using a real social network dataset, how archetypal social graph features can lead to inferences about private communication. Our results indicate that both friendship graph and public traffic data may not be informative enough to enable these inferences, with time analysis having a non-negligible impact on their precision
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
Evolving Social Networks via Friend Recommendations
A social network grows over a period of time with the formation of new
connections and relations. In recent years we have witnessed a massive growth
of online social networks like Facebook, Twitter etc. So it has become a
problem of extreme importance to know the destiny of these networks. Thus
predicting the evolution of a social network is a question of extreme
importance. A good model for evolution of a social network can help in
understanding the properties responsible for the changes occurring in a network
structure. In this paper we propose such a model for evolution of social
networks. We model the social network as an undirected graph where nodes
represent people and edges represent the friendship between them. We define the
evolution process as a set of rules which resembles very closely to how a
social network grows in real life. We simulate the evolution process and show,
how starting from an initial network, a network evolves using this model. We
also discuss how our model can be used to model various complex social networks
other than online social networks like political networks, various
organizations etc..Comment: 5 pages, 8 figures, 2 algorithm
Do narcissism and emotional intelligence win us friends? Modeling dynamics of peer popularity using inferential network analysis
This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study we examined the dynamics of popularity in 15 peer groups in two waves (N=273).We measured narcissism, ability EI, explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and three months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity
On social networks and collaborative recommendation
Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data.
We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks.
In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.</p
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