The study explored spousal accuracy and positive marital behavior (PMB) and negative marital behavior (NMB) areas&rsquo / three different evaluations which were frequency, emotional impact and attributed importance in distressed and nondistressed couples. Participants of the study were 81 married couples. All 162 spouses filled out Positive and Negative Affect Schedule (PANAS), Dyadic Adjustment Scale (DAS), Communication Skills Inventory and Information Form. Additionally one spouse in each couple filled out Spouse Observation Checklist (SOC) Form A whereas the other spouse filled out SOC Form B. Spousal accuracy were assessed by partial pairwise intraclass correlation. R-to-z transformation was used to find on which PMB and NMB areas&rsquo / accuracy distressed and nondistressed couples differ. Six Repeated Measures MANOVAs were conducted to explore differences in distressed and nondistressed couples / wives and husbands / self-report and spouse-report in three evaluations of PMB and NMB. In order to find most important PMB and NMB areas&rsquo / frequencies in terms of their relationship with marital adjustment, two Roy-Bargmann Stepdown Analysis were conducted by controlling for positive affect, negative affect and communication skills. Principal component analysis was employed to the self and spouse reports of marital behavior areas&rsquo / frequencies and then two stepwise multiple regression analyses were used to identify which factors of marital behavior play a significant role in predicting marital adjustment. Results revealed that nondistressed spouses were more accurate in predicting their partners&rsquo / reports of emotional impact and attributed importance / more frequently engaging in PMB, less frequently engaging in NMB, feel more positive about and attribute more importance to PMB compared to distressed spouses. Spouse report of marital behavior explained more variance than self report of marital behavior / NMB and affectional marital behavior explained more variance than PMB in marital adjustment.

abstract: Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast amounts of data that can be structured into attributed networks, and their use has been attractive to researchers and practitioners in different disciplines. For example, in social media, users interact with each other and also post personalized content; in scientific collaboration, researchers cooperate and are distinct from peers by their unique research interests; in complex diseases studies, rich gene expression complements to the gene-regulatory networks. Clearly, attributed networks are ubiquitous and form a critical component of modern information infrastructure. To gain deep insights from such networks, it requires a fundamental understanding of their unique characteristics and be aware of the related computational challenges. My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

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Yes / The exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes. / Supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904).

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Ce travail de thèse concerne la fouille de données sur des graphes attribués. Il s'agit de graphes dans lesquels des propriétés, encodées sous forme d'attributs, sont associées à chaque sommet. Notre objectif est la découverte, dans ce type de données, de sous-graphes organisés en plusieurs groupes de sommets fortement connectés et homogènes au regard des attributs. Plus précisément, nous définissons l'extraction sous contraintes d'ensembles de sous-graphes densément connectés et tels que les sommets partagent suffisamment d'attributs. Pour cela nous proposons deux familles de motifs originales ainsi que les algorithmes justes et complets permettant leur extraction efficace sous contraintes. La première famille, nommée Ensembles Maximaux de Cliques Homogènes, correspond à des motifs satisfaisant des contraintes concernant le nombre de sous-graphes denses, la taille de ces sous-graphes et le nombre d'attributs partagés. La seconde famille, nommée Collections Homogènes de k-cliques Percolées emploie quant à elle une notion de densité plus relaxée permettant d'adapter la méthode aux données avec des valeurs manquantes. Ces deux méthodes sont appliquées à l'analyse de deux types de réseaux, les réseaux de coopérations entre chercheurs et les réseaux d'interactions de protéines. Les motifs obtenus mettent en évidence des structures utiles dans un processus de prise de décision. Ainsi, dans un réseau de coopérations entre chercheurs, l'analyse de ces structures peut aider à la mise en place de collaborations scientifiques entre des groupes travaillant sur un même domaine. Dans le contexte d'un graphe de protéines, les structures exhibées permettent d'étudier les relations entre des modules de protéines intervenant dans des situations biologiques similaires. L'étude des performances en fonction de différentes caractéristiques de graphes attribués réels et synthétiques montre que les approches proposées sont utilisables sur de grands jeux de données. / The work presented in this thesis deals with data mining approaches for the analysis of attributed graphs. An attributed graph is a graph where properties, encoded by means of attributes, are associated to each vertex. In such data, our objective is the discovery of subgraphs formed by several dense groups of vertices that are homogeneous with respect to the attributes. More precisely, we define the constraint-based extraction of collections of subgraphs densely connected and such that the vertices share enough attributes. To this aim, we propose two new classes of patterns along with sound and complete algorithms to compute them efficiently using constraint-based approaches. The first family of patterns, named Maximal Homogeneous Clique Set (MHCS), contains patterns satisfying constraints on the number of dense subgraphs, on the size of these subgraphs, and on the number of shared attributes. The second class of patterns, named Collection of Homogeneous k-clique Percolated components (CoHoP), is based on a relaxed notion of density in order to handle missing values. Both approaches are used for the analysis of scientific collaboration networks and protein-protein interaction networks. The extracted patterns exhibit structures useful in a decision support process. Indeed, in a scientific collaboration network, the analysis of such structures might give hints to propose new collaborations between researchers working on the same subjects. In a protein-protein interaction network, the analysis of the extracted patterns can be used to study the relationships between modules of proteins involved in similar biological situations. The analysis of the performances, on real and synthetic data, with respect to different attributed graph characteristics, shows that the proposed approaches scale well for large datasets.

This thesis focuses on mining user access patterns from netflow database collected from the core router of a regional network center. We use the attributed relational graph representation to formulate user access patterns on the Internet, and then propose a procedure to generalize common connection patterns and detect deviation patterns with such methods as large graph generalization, error correcting graph matching, frontier identification and pattern base recognition. The major contributions of this thesis are on represeting the network connection with attributed relational graph and developing data mining tehcniques for identifying access paterns and detecting deviation. The results can be used for better managing regional network in order to improve user satification in using regional netwrok netwrok services.

This thesis proposes an incremental algorithm to discover regional network access patterns from traffic data of a regional network. Because the size of network traffic database is very large, we need to develop a fast algorithm of association rules in order to efficiently generate user access patterns. Attributed relational graph is used to represent user access patterns on the network. The change of relational graph indicates the access pattern of a regional network is changed. In order to keep the network access pattern up to date without spending great computation costs, we propose an incremental procedure to generalize network access patterns from time to time. The results can be used for supporting network administrators to easily keep track of network usage patterns and better manage regional networks

A new operationalization was used to model a schema-based approach to moral judgment, as well as compare it to predictions based on the Social Intuitionist Model. Judgments were made about the moral wrongness of killing different animals. At Time 1, only moral judgments were made. At Time 2 judgments were made again, with questions and scales relating to attributing morally relevant cognitive capacities also included; further, two randomized conditions varied the presentation order of the scales. Differences between Time 1 and 2 indicated a reversed perspective-taking effect, with animals of lower capacities rated less empathically at Time 2. Affective ratings and attributed capacities were compared as different predictors, showing attributed capacities being more powerful. A group comparison was also made between active animal rights proponents and non-proponents, showing differences on several factors. These and other findings are discussed with relation to the Social Intuitionist Model and a schema-based account of morality.