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General random graphs (i.e., stochastic models for networks incorporating heterogeneity and/or dependence among edges) are increasingly in wide use in the study of social and other networks, but few techniques other than simulation have been available for studying their behavior. On the other hand, random graphs with independent edges (i.e., the Bernoulli graphs) are well-studied, and a large literature exists regarding their properties. In this paper, we demonstrate a method for leveraging this knowledge by constructing families of Bernoulli graphs that bound the behavior of an arbitrary random graph in a well-defined sense. By studying the behavior of these Bernoulli graph bounds, we can thus constrain the properties of a given random graph. We illustrate the utility of this approach via application to several problems from the social network literature, including identifying degeneracy in Markov graph models, studying the potential impact of tie formation mechanisms on epidemic potential in sexual contact networks, and robustness testing of inhomogeneous Bernoulli models based on geographical covariates. Practical heuristics for assessing bound tightness and guidance for use in theoretical and methodological applications are also discussed.

Different types of data privacy techniques have been applied to graphs and social networks. They have been used under different assumptions on intruders' knowledge. i.e., different assumptions on what can lead to disclosure. The analysis of different methods is also led by how data protection techniques influence the analysis of the data. i.e., information loss or data utility. One of the techniques proposed for graph is graph perturbation. Several algorithms have been proposed for this purpose. They proceed adding or removing edges, although some also consider adding and removing nodes. In this paper we propose the study of these graph perturbation techniques from a different perspective. Following the model of standard database perturbation as noise addition, we propose to study graph perturbation as noise graph addition. We think that changing the perspective of graph sanitization in this direction will permit to study the properties of perturbed graphs in a more systematic way. ; CC BY 4.0 Also part of the Security and Cryptology book sub series (LNSC, volume 11737) This work was partially supported by the Swedish Research Council (Vetenskapsrådet) project DRIAT (VR 2016-03346), the Spanish Government under grants RTI2018-095094-B-C22 "CONSENT" and TIN2014-57364-C2-2-R "SMARTGLACIS", and the UOC postdoctoral fellowship program.

Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Author Bio -- Abbreviations -- Symbols -- CHAPTER 1: General introduction -- 1.1. SAMPLING FROM FINITE POPULATIONS -- 1.2. GRAPH, MOTIF, GRAPH PARAMETER -- 1.3. OBSERVATION PROCEDURE -- 1.4. SAMPLE GRAPH, SAMPLING METHOD AND SAMPLING STRATEGY -- BIBLIOGRAPHIC NOTES -- CHAPTER 2: Bipartite incidence graph sampling and weighting -- 2.1. BIPARTITE INCIDENCE GRAPH SAMPLING -- 2.2. INCIDENCE WEIGHTING ESTIMATOR -- 2.3. RAO-BLACKWELLISATION -- 2.4. ILLUSTRATIONS -- BIBLIOGRAPHIC NOTES -- CHAPTER 3: Strategy BIGS-IWE -- 3.1. APPLICABILITY -- 3.2. NETWORK SAMPLING -- 3.3. LINE-INTERCEPT SAMPLING -- 3.4. SAMPLING FROM RELATIONAL DATABASES -- BIBLIOGRAPHIC NOTES -- CHAPTER 4: Adaptive cluster sampling -- 4.1. SPATIAL ACS -- 4.2. EPIDEMIC PREVALENCE ESTIMATION -- 4.3. ACS DESIGNS OVER TIME -- BIBLIOGRAPHIC NOTES -- CHAPTER 5: Snowball sampling -- 5.1. T-WAVE SNOWBALL SAMPLING -- 5.2. STRATEGIES FOR TSBS -- 5.3. ILLUSTRATIONS -- BIBLIOGRAPHIC NOTES -- CHAPTER 6: Targeted random walk sampling -- 6.1. RANDOM WALK IN GRAPHS -- 6.2. TARGETED RANDOM WALK -- 6.3. STRATEGY FOR TRW SAMPLING -- 6.4. ILLUSTRATIONS -- BIBLIOGRAPHIC NOTES -- Bibliography -- Index.

Altres ajuts: This project has received funding from the European Union's Horinon 2020 research and innovation programme. This work was also partially funded by the Ministerio de Economía y Competitividad. The authors also acknowledge the funding from the Academy of Finland (Grants 276376, 284548, 295777, 304666, 312294, 312297, 312551, and 314810), TEKES-the Finnish Funding Agency for Technology and Innovation. The authors also thank Dr. Stephan Suckow in AMO GmbH for fruitful discussions about photonic device behavior. ; Because of their extraordinary physical properties, low-dimensional materials including graphene and gallium selenide (GaSe) are promising for future electronic and optoelectronic applications, particularly in transparent-flexible photodetectors. Currently, the photodetectors working at the near-infrared spectral range are highly indispensable in optical communications. However, the current photodetector architectures are typically complex, and it is normally difficult to control the architecture parameters. Here, we report graphene-GaSe heterojunction-based field-effect transistors with broadband photodetection from 730-1550 nm. Chemical-vapor-deposited graphene was employed as transparent gate and contact electrodes with tunable resistance, which enables effective photocurrent generation in the heterojunctions. The photoresponsivity was shown from 10 to 0.05 mA/W in the near-infrared region under the gate control. To understand behavior of the transistor, we analyzed the results via simulation performed using a model for the gate-tunable graphene-semiconductor heterojunction where possible Fermi level pinning effect is considered.

Data privacy is a major problem that has to be considered before releasing datasets to the public or even to a partner company that would compute statistics or make a deep analysis of these data. Privacy is insured by performing data anonymization as required by legislation. In this context, many different anonymization techniques have been proposed in the literature. These techniques are difficult to use in a general context where attacks can be of different types, and where measures are not known to the anonymizer. Generic methods able to adapt to different situations become desirable. We are addressing the problem of privacy related to graph data which needs, for different reasons, to be publicly made available. This corresponds to the anonymized graph data publishing problem. We are placing from the perspective of an anonymizer not having access to the methods used to analyze the data. A generic methodology is proposed based on machine learning to obtain directly an anonymization function from a set of training data so as to optimize a tradeoff between privacy risk and utility loss. The method thus allows one to get a good anonymization procedure for any kind of attacks, and any characteristic in a given set. The methodology is instantiated for simple graphs and complex timestamped graphs. A tool has been developed implementing the method and has been experimented with success on real anonymized datasets coming from Twitter, Enron or Amazon. Results are compared with baseline and it is showed that the proposed method is generic and can automatically adapt itself to different anonymization contexts. ; La confidentialité des données est un problème majeur qui doit être considéré avant de rendre publiques les données ou avant de les transmettre à des partenaires tiers avec comme but d'analyser ou de calculer des statistiques sur ces données. Leur confidentialité est principalement préservée en utilisant des techniques d'anonymisation. Dans ce contexte, un nombre important de techniques d'anonymisation a été proposé dans la littérature. Cependant, des méthodes génériques capables de s'adapter à des situations variées sont souhaitables. Nous adressons le problème de la confidentialité des données représentées sous forme de graphe, données qui nécessitent, pour différentes raisons, d'être rendues publiques. Nous considérons que l'anonymiseur n'a pas accès aux méthodes utilisées pour analyser les données. Une méthodologie générique est proposée basée sur des techniques d'apprentissage artificiel afin d'obtenir directement une fonction d'anonymisation et d'optimiser la balance entre le risque pour la confidentialité et la perte dans l'utilité des données. La méthodologie permet d'obtenir une bonne procédure d'anonymisation pour une large catégorie d'attaques et des caractéristiques à préserver dans un ensemble de données. La méthodologie est instanciée pour des graphes simples et des graphes dynamiques avec une composante temporelle. La méthodologie a été expérimentée avec succès sur des ensembles de données provenant de Twitter, Enron ou Amazon. Les résultats sont comparés avec des méthodes de référence et il est montré que la méthodologie proposée est générique et peut s'adapter automatiquement à différents contextes d'anonymisation.

Data privacy is a major problem that has to be considered before releasing datasets to the public or even to a partner company that would compute statistics or make a deep analysis of these data. Privacy is insured by performing data anonymization as required by legislation. In this context, many different anonymization techniques have been proposed in the literature. These techniques are difficult to use in a general context where attacks can be of different types, and where measures are not known to the anonymizer. Generic methods able to adapt to different situations become desirable. We are addressing the problem of privacy related to graph data which needs, for different reasons, to be publicly made available. This corresponds to the anonymized graph data publishing problem. We are placing from the perspective of an anonymizer not having access to the methods used to analyze the data. A generic methodology is proposed based on machine learning to obtain directly an anonymization function from a set of training data so as to optimize a tradeoff between privacy risk and utility loss. The method thus allows one to get a good anonymization procedure for any kind of attacks, and any characteristic in a given set. The methodology is instantiated for simple graphs and complex timestamped graphs. A tool has been developed implementing the method and has been experimented with success on real anonymized datasets coming from Twitter, Enron or Amazon. Results are compared with baseline and it is showed that the proposed method is generic and can automatically adapt itself to different anonymization contexts. ; La confidentialité des données est un problème majeur qui doit être considéré avant de rendre publiques les données ou avant de les transmettre à des partenaires tiers avec comme but d'analyser ou de calculer des statistiques sur ces données. Leur confidentialité est principalement préservée en utilisant des techniques d'anonymisation. Dans ce contexte, un nombre important de techniques d'anonymisation a été ...

Data privacy is a major problem that has to be considered before releasing datasets to the public or even to a partner company that would compute statistics or make a deep analysis of these data. Privacy is insured by performing data anonymization as required by legislation. In this context, many different anonymization techniques have been proposed in the literature. These techniques are difficult to use in a general context where attacks can be of different types, and where measures are not known to the anonymizer. Generic methods able to adapt to different situations become desirable. We are addressing the problem of privacy related to graph data which needs, for different reasons, to be publicly made available. This corresponds to the anonymized graph data publishing problem. We are placing from the perspective of an anonymizer not having access to the methods used to analyze the data. A generic methodology is proposed based on machine learning to obtain directly an anonymization function from a set of training data so as to optimize a tradeoff between privacy risk and utility loss. The method thus allows one to get a good anonymization procedure for any kind of attacks, and any characteristic in a given set. The methodology is instantiated for simple graphs and complex timestamped graphs. A tool has been developed implementing the method and has been experimented with success on real anonymized datasets coming from Twitter, Enron or Amazon. Results are compared with baseline and it is showed that the proposed method is generic and can automatically adapt itself to different anonymization contexts. ; La confidentialité des données est un problème majeur qui doit être considéré avant de rendre publiques les données ou avant de les transmettre à des partenaires tiers avec comme but d'analyser ou de calculer des statistiques sur ces données. Leur confidentialité est principalement préservée en utilisant des techniques d'anonymisation. Dans ce contexte, un nombre important de techniques d'anonymisation a été ...

This work aims to develop methodologies to print pinhole-free, vertically stacked heterostructures by sequential deposition of conductive graphene and dielectric h-BN nanosheet networks. We achieve this using a combination of inkjet printing and spray-coating to fabricate dielectric capacitors in a stacked graphene/BN/graphene arrangement. Impedance spectroscopy shows such heterostructures to act as series combinations of a capacitor and a resistor, with the expected dimensional dependence of the capacitance. The areal capacitance ranges from 0.24 to 1.1?nF/cm2 with an average series resistance of ?120?k?. The sprayed BN dielectrics are pinhole-free for thicknesses above 1.65??m. This development paves the way toward fabrication of all-printed, vertically integrated, multilayer devices. ; This work was primarily supported by the SFI-funded AMBER research centre (SFI/12/RC/2278) as part of the platform projects program. In addition, we acknowledge Science Foundation Ireland (11/PI/1087) and the European Research Council (SEMANTICS) for continuing support. We have also received funding from the European Union Seventh Framework Program under Grant Agreement No. 604391 Graphene Flagship