17,495 research outputs found
Topics in social network analysis and network science
This chapter introduces statistical methods used in the analysis of social
networks and in the rapidly evolving parallel-field of network science.
Although several instances of social network analysis in health services
research have appeared recently, the majority involve only the most basic
methods and thus scratch the surface of what might be accomplished.
Cutting-edge methods using relevant examples and illustrations in health
services research are provided
Interdependent policy instrument preferences: a two-mode network approach
In policymaking, actors are likely to take the preferences of others into account when strategically positioning themselves. However, there is a lack of research that conceives of policy preferences as an interdependent system. In order to analyse interdependencies, we link actors to their policy preferences in water protection, which results in an actor-instrument network. As actors exhibit multiple preferences, a complex two-mode network between actors and policies emerges. We analyse whether actors exhibit interdependent preference profiles given shared policy objectives or social interactions among them. By fitting an exponential random graph model to the actor-instrument network, we find considerable clustering, meaning that actors tend to exhibit preferences for multiple policy instruments in common. Actors tend to exhibit interdependent policy preferences when they are interconnected, that is, they collaborate with each other. By contrast, actors are less likely to share policy preferences when a conflict line divides them
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Mutual Trust and Cooperation in the Evolutionary Hawks-Doves Game
Using a new dynamical network model of society in which pairwise interactions
are weighted according to mutual satisfaction, we show that cooperation is the
norm in the Hawks-Doves game when individuals are allowed to break ties with
undesirable neighbors and to make new acquaintances in their extended
neighborhood. Moreover, cooperation is robust with respect to rather strong
strategy perturbations. We also discuss the empirical structure of the emerging
networks, and the reasons that allow cooperators to thrive in the population.
Given the metaphorical importance of this game for social interaction, this is
an encouraging positive result as standard theory for large mixing populations
prescribes that a certain fraction of defectors must always exist at
equilibrium.Comment: 23 pages 12 images, to appea
Methods of Hierarchical Clustering
We survey agglomerative hierarchical clustering algorithms and discuss
efficient implementations that are available in R and other software
environments. We look at hierarchical self-organizing maps, and mixture models.
We review grid-based clustering, focusing on hierarchical density-based
approaches. Finally we describe a recently developed very efficient (linear
time) hierarchical clustering algorithm, which can also be viewed as a
hierarchical grid-based algorithm.Comment: 21 pages, 2 figures, 1 table, 69 reference
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