950 resultados para Symmetric Even Graphs


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In this paper, we develop a new entropic matching kernel for weighted graphs by aligning depth-based representations. We demonstrate that this kernel can be seen as an aligned subtree kernel that incorporates explicit subtree correspondences, and thus addresses the drawback of neglecting the relative locations between substructures that arises in the R-convolution kernels. Experiments on standard datasets demonstrate that our kernel can easily outperform state-of-the-art graph kernels in terms of classification accuracy.

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The popularity of online social media platforms provides an unprecedented opportunity to study real-world complex networks of interactions. However, releasing this data to researchers and the public comes at the cost of potentially exposing private and sensitive user information. It has been shown that a naive anonymization of a network by removing the identity of the nodes is not sufficient to preserve users’ privacy. In order to deal with malicious attacks, k -anonymity solutions have been proposed to partially obfuscate topological information that can be used to infer nodes’ identity. In this paper, we study the problem of ensuring k anonymity in time-varying graphs, i.e., graphs with a structure that changes over time, and multi-layer graphs, i.e., graphs with multiple types of links. More specifically, we examine the case in which the attacker has access to the degree of the nodes. The goal is to generate a new graph where, given the degree of a node in each (temporal) layer of the graph, such a node remains indistinguishable from other k-1 nodes in the graph. In order to achieve this, we find the optimal partitioning of the graph nodes such that the cost of anonymizing the degree information within each group is minimum. We show that this reduces to a special case of a Generalized Assignment Problem, and we propose a simple yet effective algorithm to solve it. Finally, we introduce an iterated linear programming approach to enforce the realizability of the anonymized degree sequences. The efficacy of the method is assessed through an extensive set of experiments on synthetic and real-world graphs.

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Kernel methods provide a way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that of defining a positive semidefinite kernel. In this paper, we propose a novel kernel on unattributed graphs where the structure is characterized through the evolution of a continuous-time quantum walk. More precisely, given a pair of graphs, we create a derived structure whose degree of symmetry is maximum when the original graphs are isomorphic. With this new graph to hand, we compute the density operators of the quantum systems representing the evolutions of two suitably defined quantum walks. Finally, we define the kernel between the two original graphs as the quantum Jensen-Shannon divergence between these two density operators. The experimental evaluation shows the effectiveness of the proposed approach. © 2013 Springer-Verlag.

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In this paper we propose a prototype size selection method for a set of sample graphs. Our first contribution is to show how approximate set coding can be extended from the vector to graph domain. With this framework to hand we show how prototype selection can be posed as optimizing the mutual information between two partitioned sets of sample graphs. We show how the resulting method can be used for prototype graph size selection. In our experiments, we apply our method to a real-world dataset and investigate its performance on prototype size selection tasks. © 2012 Springer-Verlag Berlin Heidelberg.

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2000 Mathematics Subject Classification: Primary: 47B47, 47B10; secondary 47A30.

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2000 Mathematics Subject Classification: 12D10.

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MSC 2010: 35J05, 33C10, 45D05

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2000 Mathematics Subject Classification: Primary 60J45, 60J50, 35Cxx; Secondary 31Cxx.

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2000 Mathematics Subject Classification: 15A69, 15A78.

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2000 Mathematics Subject Classification: Primary 30C45, secondary 30C80.

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2010 Mathematics Subject Classification: 05C38, 05C45.

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2010 Mathematics Subject Classification: 05C50.

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Based upon unique survey data collected using respondent driven sampling methods, we investigate whether there is a gender pay gap among social entrepreneurs in the UK. We find that women as social entrepreneurs earn 29% less than their male colleagues, above the average UK gender pay gap of 19%. We estimate the adjusted pay gap to be about 23% after controlling for a range of demographic, human capital and job characteristics, as well as personal preferences and values. These differences are hard to explain by discrimination since these CEOs set their own pay. Income may not be the only aim in an entrepreneurial career, so we also look at job satisfaction to proxy for non-monetary returns. We find female social entrepreneurs to be more satisfied with their job as a CEO of a social enterprise than their male counterparts. This result holds even when we control for the salary generated through the social enterprise. Our results extend research in labour economics on the gender pay gap as well as entrepreneurship research on women’s entrepreneurship to the novel context of social enterprise. It provides the first evidence for a “contented female social entrepreneur” paradox.