61,423 research outputs found
Causal inference for social network data
We describe semiparametric estimation and inference for causal effects using
observational data from a single social network. Our asymptotic result is the
first to allow for dependence of each observation on a growing number of other
units as sample size increases. While previous methods have generally
implicitly focused on one of two possible sources of dependence among social
network observations, we allow for both dependence due to transmission of
information across network ties, and for dependence due to latent similarities
among nodes sharing ties. We describe estimation and inference for new causal
effects that are specifically of interest in social network settings, such as
interventions on network ties and network structure. Using our methods to
reanalyze the Framingham Heart Study data used in one of the most influential
and controversial causal analyses of social network data, we find that after
accounting for network structure there is no evidence for the causal effects
claimed in the original paper
Local Ranking Problem on the BrowseGraph
The "Local Ranking Problem" (LRP) is related to the computation of a
centrality-like rank on a local graph, where the scores of the nodes could
significantly differ from the ones computed on the global graph. Previous work
has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a
graph where nodes are webpages and edges are browsing transitions. Recently,
this graph has received more and more attention in many different tasks such as
ranking, prediction and recommendation. However, a web-server has only the
browsing traffic performed on its pages (local BrowseGraph) and, as a
consequence, the local computation can lead to estimation errors, which hinders
the increasing number of applications in the state of the art. Also, although
the divergence between the local and global ranks has been measured, the
possibility of estimating such divergence using only local knowledge has been
mainly overlooked. These aspects are of great interest for online service
providers who want to: (i) gauge their ability to correctly assess the
importance of their resources only based on their local knowledge, and (ii)
take into account real user browsing fluxes that better capture the actual user
interest than the static hyperlink network. We study the LRP problem on a
BrowseGraph from a large news provider, considering as subgraphs the
aggregations of browsing traces of users coming from different domains. We show
that the distance between rankings can be accurately predicted based only on
structural information of the local graph, being able to achieve an average
rank correlation as high as 0.8
Resolving Multi-party Privacy Conflicts in Social Media
Items shared through Social Media may affect more than one user's privacy ---
e.g., photos that depict multiple users, comments that mention multiple users,
events in which multiple users are invited, etc. The lack of multi-party
privacy management support in current mainstream Social Media infrastructures
makes users unable to appropriately control to whom these items are actually
shared or not. Computational mechanisms that are able to merge the privacy
preferences of multiple users into a single policy for an item can help solve
this problem. However, merging multiple users' privacy preferences is not an
easy task, because privacy preferences may conflict, so methods to resolve
conflicts are needed. Moreover, these methods need to consider how users' would
actually reach an agreement about a solution to the conflict in order to
propose solutions that can be acceptable by all of the users affected by the
item to be shared. Current approaches are either too demanding or only consider
fixed ways of aggregating privacy preferences. In this paper, we propose the
first computational mechanism to resolve conflicts for multi-party privacy
management in Social Media that is able to adapt to different situations by
modelling the concessions that users make to reach a solution to the conflicts.
We also present results of a user study in which our proposed mechanism
outperformed other existing approaches in terms of how many times each approach
matched users' behaviour.Comment: Authors' version of the paper accepted for publication at IEEE
Transactions on Knowledge and Data Engineering, IEEE Transactions on
Knowledge and Data Engineering, 201
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
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