430 research outputs found
Topology and correlations in structured scale-free networks
We study a recently introduced class of scale-free networks showing a high
clustering coefficient and non-trivial connectivity correlations. We find that
the connectivity probability distribution strongly depends on the fine details
of the model. We solve exactly the case of low average connectivity, providing
also exact expressions for the clustering and degree correlation functions. The
model also exhibits a lack of small world properties in the whole parameters
range. We discuss the physical properties of these networks in the light of the
present detailed analysis.Comment: 10 pages, 9 figure
Local Algorithms for Finding Densely Connected Clusters
Local graph clustering is an important algorithmic technique for analysing
massive graphs, and has been widely applied in many research fields of data
science. While the objective of most (local) graph clustering algorithms is to
find a vertex set of low conductance, there has been a sequence of recent
studies that highlight the importance of the inter-connection between clusters
when analysing real-world datasets. Following this line of research, in this
work we study local algorithms for finding a pair of vertex sets defined with
respect to their inter-connection and their relationship with the rest of the
graph. The key to our analysis is a new reduction technique that relates the
structure of multiple sets to a single vertex set in the reduced graph. Among
many potential applications, we show that our algorithms successfully recover
densely connected clusters in the Interstate Disputes Dataset and the US
Migration Dataset.Comment: This work is accepted at ICML'21 for a long presentatio
An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach
Abstract: New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique
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