185 research outputs found
Social Ranking Techniques for the Web
The proliferation of social media has the potential for changing the
structure and organization of the web. In the past, scientists have looked at
the web as a large connected component to understand how the topology of
hyperlinks correlates with the quality of information contained in the page and
they proposed techniques to rank information contained in web pages. We argue
that information from web pages and network data on social relationships can be
combined to create a personalized and socially connected web. In this paper, we
look at the web as a composition of two networks, one consisting of information
in web pages and the other of personal data shared on social media web sites.
Together, they allow us to analyze how social media tunnels the flow of
information from person to person and how to use the structure of the social
network to rank, deliver, and organize information specifically for each
individual user. We validate our social ranking concepts through a ranking
experiment conducted on web pages that users shared on Google Buzz and Twitter.Comment: 7 pages, ASONAM 201
Seeding for pervasively overlapping communities
In some social and biological networks, the majority of nodes belong to
multiple communities. It has recently been shown that a number of the
algorithms that are designed to detect overlapping communities do not perform
well in such highly overlapping settings. Here, we consider one class of these
algorithms, those which optimize a local fitness measure, typically by using a
greedy heuristic to expand a seed into a community. We perform synthetic
benchmarks which indicate that an appropriate seeding strategy becomes
increasingly important as the extent of community overlap increases. We find
that distinct cliques provide the best seeds. We find further support for this
seeding strategy with benchmarks on a Facebook network and the yeast
interactome.Comment: 8 Page
Minimization via duality
We show how to use duality theory to construct minimized versions of a wide class of automata. We work out three cases in detail: (a variant of) ordinary automata, weighted automata and probabilistic automata. The basic idea is that instead of constructing a maximal quotient we go to the dual and look for a minimal subalgebra and then return to the original category. Duality ensures that the minimal subobject becomes the maximally quotiented object
Online Popularity and Topical Interests through the Lens of Instagram
Online socio-technical systems can be studied as proxy of the real world to
investigate human behavior and social interactions at scale. Here we focus on
Instagram, a media-sharing online platform whose popularity has been rising up
to gathering hundred millions users. Instagram exhibits a mixture of features
including social structure, social tagging and media sharing. The network of
social interactions among users models various dynamics including
follower/followee relations and users' communication by means of
posts/comments. Users can upload and tag media such as photos and pictures, and
they can "like" and comment each piece of information on the platform. In this
work we investigate three major aspects on our Instagram dataset: (i) the
structural characteristics of its network of heterogeneous interactions, to
unveil the emergence of self organization and topically-induced community
structure; (ii) the dynamics of content production and consumption, to
understand how global trends and popular users emerge; (iii) the behavior of
users labeling media with tags, to determine how they devote their attention
and to explore the variety of their topical interests. Our analysis provides
clues to understand human behavior dynamics on socio-technical systems,
specifically users and content popularity, the mechanisms of users'
interactions in online environments and how collective trends emerge from
individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201
Large-scale diversity estimation through surname origin inference
The study of surnames as both linguistic and geographical markers of the past
has proven valuable in several research fields spanning from biology and
genetics to demography and social mobility. This article builds upon the
existing literature to conceive and develop a surname origin classifier based
on a data-driven typology. This enables us to explore a methodology to describe
large-scale estimates of the relative diversity of social groups, especially
when such data is scarcely available. We subsequently analyze the
representativeness of surname origins for 15 socio-professional groups in
France
It's not stealing if you need it: A panel on the ethics of performing research using public data of illicit origin
In a world where sensitive data can be published to a worldwide audience with the press of a button, researchers are increasingly making use of datasets that were publicized under questionable circumstances. In many cases, such research would otherwise not
Towards real-time community detection in large networks
The recent boom of large-scale Online Social Networks (OSNs) both enables and
necessitates the use of parallelisable and scalable computational techniques
for their analysis. We examine the problem of real-time community detection and
a recently proposed linear time - O(m) on a network with m edges - label
propagation or "epidemic" community detection algorithm. We identify
characteristics and drawbacks of the algorithm and extend it by incorporating
different heuristics to facilitate reliable and multifunctional real-time
community detection. With limited computational resources, we employ the
algorithm on OSN data with 1 million nodes and about 58 million directed edges.
Experiments and benchmarks reveal that the extended algorithm is not only
faster but its community detection accuracy is compared favourably over popular
modularity-gain optimization algorithms known to suffer from their resolution
limits.Comment: 10 pages, 11 figure
Beating the news using social media: the case study of American Idol
We present a contribution to the debate on the predictability of social events using big data analytics. We focus on the elimination of contestants in the American Idol TV shows as an example of a well defined electoral phenomenon that each week draws millions of votes in the USA. This event can be considered as basic test in a simplified environment to assess the predictive power of Twitter signals. We provide evidence that Twitter activity during the time span defined by the TV show airing and the voting period following it correlates with the contestants ranking and allows the anticipation of the voting outcome. Twitter data from the show and the voting period of the season finale have been analyzed to attempt the winner prediction ahead of the airing of the official result. We also show that the fraction of tweets that contain geolocation information allows us to map the fanbase of each contestant, both within the US and abroad, showing that strong regional polarizations occur. The geolocalized data are crucial for the correct prediction of the final outcome of the show, pointing out the importance of considering information beyond the aggregated Twitter signal. Although American Idol voting is just a minimal and simplified version of complex societal phenomena such as political elections, this work shows that the volume of information available in online systems permits the real time gathering of quantitative indicators that may be able to anticipate the future unfolding of opinion formation events
Characterising Probabilistic Processes Logically
In this paper we work on (bi)simulation semantics of processes that exhibit
both nondeterministic and probabilistic behaviour. We propose a probabilistic
extension of the modal mu-calculus and show how to derive characteristic
formulae for various simulation-like preorders over finite-state processes
without divergence. In addition, we show that even without the fixpoint
operators this probabilistic mu-calculus can be used to characterise these
behavioural relations in the sense that two states are equivalent if and only
if they satisfy the same set of formulae.Comment: 18 page
Dynamics in online social networks
An increasing number of today's social interactions occurs using online
social media as communication channels. Some online social networks have become
extremely popular in the last decade. They differ among themselves in the
character of the service they provide to online users. For instance, Facebook
can be seen mainly as a platform for keeping in touch with close friends and
relatives, Twitter is used to propagate and receive news, LinkedIn facilitates
the maintenance of professional contacts, Flickr gathers amateurs and
professionals of photography, etc. Albeit different, all these online platforms
share an ingredient that pervades all their applications. There exists an
underlying social network that allows their users to keep in touch with each
other and helps to engage them in common activities or interactions leading to
a better fulfillment of the service's purposes. This is the reason why these
platforms share a good number of functionalities, e.g., personal communication
channels, broadcasted status updates, easy one-step information sharing, news
feeds exposing broadcasted content, etc. As a result, online social networks
are an interesting field to study an online social behavior that seems to be
generic among the different online services. Since at the bottom of these
services lays a network of declared relations and the basic interactions in
these platforms tend to be pairwise, a natural methodology for studying these
systems is provided by network science. In this chapter we describe some of the
results of research studies on the structure, dynamics and social activity in
online social networks. We present them in the interdisciplinary context of
network science, sociological studies and computer science.Comment: 17 pages, 4 figures, book chapte
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