Academic literature on the topic 'Graph, social and multimedia data'

The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results, but also leads to more complex graph structures. However, graph traversal-based algorithms for similarity are quite inefficient and computationally expensive, especially for large data structures. To deliver fast and effective retrieval especially for large multimedia collections and multimedia big data, an efficient similarity algorithm for large graphs in particular is desirable. Hence, in this paper, we define a graph projection into a 2D space (Graph Code) and the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph traversals due to the simpler processing model and the high level of parallelization. As a consequence, we demonstrate experimentally that the effectiveness of retrieval also increases substantially, as the Graph Code facilitates more levels of detail in feature fusion. These levels of detail also support an increased trust prediction, particularly for fused social media content. In our mathematical model, we define a metric triple for the Graph Code, which also enhances the ranked result representations. Thus, Graph Codes provide a significant increase in efficiency and effectiveness, especially for multimedia indexing and retrieval, and can be applied to images, videos, text and social media information.

Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, the authors aim at measuring the semantic relatedness of Flickr images. Firstly, information theory based functions are used to measure the semantic relatedness of tags. Secondly, the integration of tags pair based on bipartite graph is proposed to remove the noise and redundancy. The data sets including 1000 images from Flickr are used to evaluate the proposed method. Two data mining tasks including clustering and searching are performed by the proposed method, which shows the effectiveness and robust of the proposed method.

Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, we aim at measuring the semantic relatedness of Flickr images. Firstly, four information theory based functions are used to measure the semantic relatedness of tags. Secondly, the integration of tags pair based on bipartite graph is proposed to remove the noise and redundancy. Thirdly, the order information of tags is added to measure the semantic relatedness, which emphasizes the tags with high positions. The data sets including 1000 images from Flickr are used to evaluate the proposed method. Two data mining tasks including clustering and searching are performed by the proposed method, which shows the effectiveness and robustness of the proposed method. Moreover, some applications such as searching and faceted exploration are introduced using the proposed method, which shows that the proposed method has broad prospects on web based tasks.

The object of the research are methods and algorithms of optimizing of the Big Data transformation to build a social profile model, the subject of the research are methods of constructing of a social profile. For decision-making person, the problem of scientific methodological and instrumental re-equipment is relevant for the effective fulfillment of a set of managerial tasks and confronting of fundamentally new challenges and threats in society. This task is directly related to the problem of building of a model of the social profile of both the individual and the social group as a whole. Therefore, the problem of optimizing of methods of constructing of a mathematical model of a social profile is certainly relevant. During the research, methods of the mathematical apparatus of graph theory, database theory and the concept of non-relational data stores, Big Data technology, text analytics technologies, parallel data processing methods, methods of neural networks' using, methods of multimedia data analyzing were used. These methods were integrated into the general method, called the method of increasing of the efficiency of constructing of a mathematical model of a social profile. The proposed method improves the adequacy of the social profile model, which will significantly improve and simplify the functioning of information systems for decision-making based on knowledge of the social advantages of certain social groups, which will allow dynamic correction of their behavior. The obtained results of testing the method make it possible to consider it as an effective tool for obtaining of an objective information model of a social portrait of a social group. This is because the correctness of setting and solving of the problem ensured that adequate results were obtained. Unlike the existing ones, the proposed modeling method, which uses an oriented graph, allows to improve significantly the quality and adequacy of this process. Further research should be directed towards the implementation of proposed theoretical developments in real decision-making systems. This will increase the weight of automated decision-making systems for social climate analysis.

Community platforms featuring user sharing and self-expression in social media generate big data on tourism resources, which, if fully utilized in a smart tourism system driven by high-tech and new technologies, will bring new life to the field of smart tourism research and will play an important role in the development of Internet+ tourism. However, tourism data in social media has the following characteristics: diversity, redundancy, heterogeneity, and intelligence. To address the characteristics of tourism data in social media, this thesis focuses on the following challenges: it is difficult to efficiently obtain tourism visualization information (text and images) in social media; it is difficult to effectively utilize tourism multimodal heterogeneous information; it is difficult to properly retrieve multimedia entity information of tourism attractions; and it is difficult to reasonably construct tourism personalized recommendation models. In this paper, an image search reordering method based on a hybrid feature graph model is proposed to realize the rapid acquisition of high-quality Internet images from the web using hybrid visual features and graph models, thus providing data security for the analysis of social media-based tourism images. To address the shortcomings of current search engines for image retrieval, visual information is used to bridge the problem of semantic gap between text-based search and images. To address the limitation of single visual features, we use latent semantic analysis to fuse multiple visual features to obtain hybrid features, which not only combine multiple single features but also preserve the potential relationship between these features. To address the shortcomings of the reordering methods based on classification and clustering, a reordering framework based on the graph model is used to reorder the images and finally complete the image search reordering based on the hybrid feature graph model. This method can obtain image information in social media with high efficiency and quality and then prepare for the subsequent work of tourism image analysis mining and personalized recommendation.

In this study, we propose a novel music playlist generation method based on a knowledge graph and reinforcement learning. The development of music streaming platforms has transformed the social dynamics of music consumption and paved a new way of accessing and listening to music. The playlist generation is one of the most important multimedia techniques, which aims to recommend music tracks by sensing the vast amount of musical data and the users’ listening histories from music streaming services. Conventional playlist generation methods have difficulty capturing the target users’ long-term preferences. To overcome the difficulty, we use a reinforcement learning algorithm that can consider the target users’ long-term preferences. Furthermore, we introduce the following two new ideas: using the informative knowledge graph data to promote efficient optimization through reinforcement learning, and setting the flexible reward function that target users can design the parameters of itself to guide target users to new types of music tracks. We confirm the effectiveness of the proposed method by verifying the prediction performance based on listening history and the guidance performance to music tracks that can satisfy the target user’s unique preference.

Social Internet of Things (SIoT) is an emerging field that combines IoT and Internet, which can provide many novel and convenient application scenarios but still faces challenges in data privacy protection. In this paper, we propose a robust behavioral steganography method with high embedding capacity across social networks based on timestamp modulation. Firstly, the IoT devices on the sending end modulate the secret message to be embedded into a timestamp by using the common property on social networks. Secondly, the accounts of multiple social networks are used as the vertices, and the timestamp mapping relationship generated by the interaction behaviors between them is used as the edges to construct a directed secret message graph across social networks. Then, the frequency of interaction behaviors generated by users of mainstream social networks is analyzed; the corresponding timestamps and social networks are used to implement interaction behaviors based on the secret message graph and the frequency of interaction behaviors. Next, we analyze the frequency of interaction behaviors generated by users in mainstream social networks, implement the interaction behaviors according to the secret message graph and the frequency of interaction behaviors in the corresponding timestamps and social networks, and combine the redundant mapping control to complete the embedding of secret message. Finally, the receiver constructs the timestamp mapping relationship through the shared account, key, and other parameters to achieve the extraction of secret message. The algorithm is robust and does not have the problem that existing multimedia-based steganography methods are difficult to extract the embedded messages completely. Compared with existing graph theory-based social network steganography methods, using timestamps and behaviors frequencies to hide message in multiple social networks increases the cost of detecting covert communication and improves concealment of steganography. At the same time, the algorithm uses a directed secret message graph to increase the number of bits carried by each behavior and improves the embedding capacity. A large number of tests have been conducted on mainstream social networks such as Facebook, Twitter, and Weibo. The results show that the proposed method successfully distributes secret message to multiple social networks and achieves complete extraction of embedded message at the receiving end. The embedding capacity is increased by 1.98–4.89 times compared with the existing methods SSN, NGTASS, and SGSIR.

Abstract: Cloud data centers become more and more powerful, energy consumption becomes a major challenge both for environmental concerns and for economic reasons, Towards this aim, we present model of social network website and will optimize server in such way that old data server should run on minimum cost. Also this implementation finds out the pattern of data consuming of users and according to that the graphs get generated. And after generating user data consumption data patterns optimize server in such way that whichever server data have less traffic should stay on sleep mode.

This paper reports on the 28th Italian Symposium on Advanced Database Systems (SEBD 2020), held online as a virtual conference from the 21st to the 24th of June 2020. The topics that were addressed in this edition of the conference were organized in the sessions: ontologies and data integration, anomaly detection and dependencies, text analysis and search, deep learning, noSQL data, trajectories and diffusion, health and medicine, context and ranking, social and knowledge graphs, multimedia content analysis, security issues, and data mining.

Mobile applications are really important nowadays due to providing the accurate check-in data for research. The primary goal of the study is to look into the impact of several forms of entertainment activities on the density dispersal of occupants in Shanghai, China, as well as prototypical check-in data from a location-based social network using a combination of temporal, spatial, and visualization techniques and categories of visitors’ check-ins. This article explores Weibo for big data assessment and its reliability in a variety of categories rather than physically obtained information by examining the link between time, frequency, place, class, and place of check-in based on geographic attributes and related implications. The data for this study came from Weibo, a popular Chinese microblog. It was preprocessed to extract the most important and associated results elements, then converted to geographical information systems format, appraised, and finally displayed using graphs, tables, and heat maps. For data significance, a linear regression model was used, and, for spatial analysis, kernel density estimation was utilized. As per results of hours-to-day usage patterns, enjoyment activities and frequency distribution are produced. Our findings are based on the check-in behaviour of users at amusement locations, the density of check-ins, rush periods for visiting amusement locations, and gender differences. Our data provide light on different elements of human behaviour patterns, the importance of entertainment venues, and their impact in Shanghai. So it can be used in pattern recognition, endorsement structures, and additional multimedia content for these collections.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Madisetti, Vijay; Committee Member: Jayant, Nikil; Committee Member: Lee, Chin-Hui; Committee Member: Ramachandran, Umakishore; Committee Member: Yalamanchili, Sudhakar. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Powered by big data infrastructures, social network platforms are gathering data on many aspects of our daily lives. The online social world is reflecting our physical world in an increasingly detailed way by collecting people's individual biographies and their various of relationships with other people. Although massive amount of social data has been gathered, an urgent challenge remain unsolved, which is to discover meaningful knowledge that can empower the social platforms to really understand their users from different perspectives.

Motivated by this trend, my research addresses the reasoning and mathematical modeling behind interesting phenomena on social networks. Proposing graph based data mining framework regarding to heterogeneous data sources is the major goal of my research. The algorithms, by design, utilize graph structure with heterogeneous link and node features to creatively represent social networks' basic structures and phenomena on top of them.

The graph based heterogeneous mining methodology is proved to be effective on a series of knowledge discovery topics, including network structure and macro social pattern mining such as magnet community detection (87), social influence propagation and social similarity mining (85), and spam detection (86). The future work is to consider dynamic relation on social data mining and how graph based approaches adapt from the new situations.

Actualmente, las redes sociales en línea son uno de los principales medios donde se intercambia gran cantidad de información. En estas, los usuarios intentan reflejar su actividad diaria en forma de publicaciones en sus muros o de otros usuarios. Asimismo, las imágenes representan gran parte de la información sobre la actividad del usuario, por ejemplo, una foto en donde esté etiquetado. Estas interacciones del usuario en las redes ayudan a generar su identidad digital. La información revelada por la metadata de las imágenes enriquece este perfil y contribuye a mejorar los resultados en procesos como minería de datos, marketing, etc. El objetivo de este proyecto es generar un perfil digital en base a la información y actividad que contribuye un usuario a una red social, recopilando y mostrando explícitamente varios hechos que se revelan aprovechando la metadata de las imágenes y el factor temporal de la actividad en línea. Esto incluye el proceso de extracción, enriquecimiento y encapsulación de data en un modelo ontológico propuesto. Los resultados de los experimentos muestran que la información en el perfil, luego del enriquecimiento, es aproximadamente cuatro veces la información inicial, y la precisión de la nueva información está por encima del 75%. Trabajos futuros se inclinan hacia la detección del tipo de relación que existe entre una persona y uno de sus contactos. Asimismo, otro tema relevante a explorar incluye la extracción de un mayor rango de entidades, tales como eventos o temas de interés de un individuo, con el fin de mejorar el perfil digital del usuario. Finalmente, la minería de datos en el proceso de extracción de información ayudaría a enfocar mejor el marketing a los usuarios de redes sociales ya que dicha publicidad podría hacerse más personalizada. Palabras clave Linked data, información multimedia, perfil digital, redes sociales, metadata
Tesis

Tesis para optar al grado de Doctora en Ciencias, Mención Computación
Buscar contenido multimedia es una de las tareas más comunes que los usuarios realizan en la Web. Actualmente, los motores de búsqueda en la Web han mejorado la precisión de sus búsquedas de contenido multimedia y ahora brindan una mejor experiencia de usuarios. Sin embargo, estos motores aún no logran obtener resultados precisos para consultas que no son comunes, y consultas que se refieren a conceptos abstractos. En ambos escenarios, la razón principal es la falta de información preliminar. Esta tesis se enfoca en mejorar la recuperación de información multimedia en la Web usando datos generados a partir de la interacción entre usuarios y recursos multimedia. Para eso, se propone mejorar la recuperación de información multimedia desde dos perspectivas: (1) extrayendo conceptos relevantes a los recursos multimedia, y (2) mejorando las descripciones multimedia con datos generados por el usuario. En ambos casos, proponemos sistemas que funcionan independientemente del tipo de multimedia, y del idioma de los datos de entrada. En cuanto a la identificación de conceptos relacionados a objetos multimedia, desarrollamos un sistema que va desde los resultados de búsqueda específicos de la consulta hasta los conceptos detectados para dicha consulta. Nuestro enfoque demuestra que podemos aprovechar la vista parcial de una gran colección de documentos multimedia para detectar conceptos relevantes para una consulta determinada. Además, diseñamos una evaluación basada en usuarios que demuestra que nuestro algoritmo de detección de conceptos es más sólido que otros enfoques similares basados en detección de comunidades. Para mejorar la descripción multimedia, desarrollamos un sistema que combina contenido audio-visual de documentos multimedia con información de su contexto para mejorar y generar nuevas anotaciones para los documentos multimedia. Específicamente, extraemos datos de clicks de los registros de consultas y usamos las consultas como sustitutos para las anotaciones manuales. Tras una primera inspección, demostramos que las consultas proporcionan una descripción concisa de los documentos multimedia. El objetivo principal de esta tesis es demostrar la relevancia del contexto asociado a documentos multimedia para mejorar el proceso de recuperación de documentos multimedia en la Web. Además, mostramos que los grafos proporcionan una forma natural de modelar problemas multimedia.
Fondef D09I-1185, CONICYT-PCHA/Doctorado Nacional/2013-63130260, Apoyo a estadías corta de la Escuela de Postgrado de la U. de Chile, y el Núcleo Milenio CIWS

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Near real-time event streams are a key feature in many popular social media applications. These types of applications allow users to selectively follow event streams to receive a curated list of real-time events from various sources. Due to the emphasis on recency, relevance, personalization of content, and the highly variable cardinality of social subgraphs, it is extremely difficult to implement feed following at the scale of major social media applications. This leads to multiple architectural approaches, but no consensus has been reached as to what is considered to be an idiomatic solution. As of today, there are various theoretical approaches exploiting the dynamic nature of social graphs, but not all of them have been applied in practice. In this paper, large-cardinality graphs are placed in the context of existing research to highlight the exceptional data management challenges that are posed for large-scale real-time social media applications. This work outlines the key characteristics of data dissemination in large-cardinality social graphs, and overviews existing research and state-of-the-art approaches in industry, with the goal of stimulating further research in this direction.

En los últimos años, ha sido puesto a disposición del público una gran cantidad de los datos con formato de grafo. Incrustado en estos datos hay información privada acerca de los usuarios que aparecen en ella. Por lo tanto, los propietarios de datos deben respetar la privacidad de los usuarios antes de liberar los conjuntos de datos a terceros. En este escenario, los procesos de anonimización se convierten en un proceso muy importante. Sin embargo, los procesos de anonimización introducen, generalmente, algún tipo de ruido en los datos anónimos y también en sus resultados en procesos de minería de datos. Generalmente, cuanto mayor la privacidad, mayor será el ruido. Por lo tanto, la utilidad de los datos es un factor importante a tener en cuenta en los procesos de anonimización. El equilibrio necesario entre la privacidad de datos y utilidad de éstos puede mejorar mediante el uso de medidas y métricas para guiar el proceso de anonimización, de tal forma que se minimice la pérdida de información. En esta tesis hemos trabajo los campos de la preservación de la privacidad del usuario en las redes sociales y la utilidad y calidad de los datos publicados. Un compromiso entre ambos campos es un punto crítico para lograr buenos métodos de anonimato, que permitan mejorar los posteriores procesos de minería de datos. Parte de esta tesis se ha centrado en la utilidad de los datos y la pérdida de información. En primer lugar, se ha estudiado la relación entre las medidas de pérdida de información genéricas y las específicas basadas en clustering, con el fin de evaluar si las medidas genéricas de pérdida de información son indicativas de la utilidad de los datos para los procesos de minería de datos posteriores. Hemos encontrado una fuerte correlación entre algunas medidas genéricas de pérdida de información (average distance, betweenness centrality, closeness centrality, edge intersection, clustering coefficient y transitivity) y el índice de precisión en los resultados de varios algoritmos de clustering, lo que demuestra que estas medidas son capaces de predecir el perturbación introducida en los datos anónimos. En segundo lugar, se han presentado dos medidas para reducir la pérdida de información en los procesos de modificación de grafos. La primera, Edge neighbourhood centrality, se basa en el flujo de información de a través de la vecindad a distancia 1 de una arista específica. El segundo se basa en el core number sequence y permite conservar mejor la estructura subyacente, mejorando la utilidad de los datos. Hemos demostrado que ambos métodos son capaces de preservar las aristas más importantes del grafo, manteniendo mejor las propiedades básicas estructurales y espectrales. El otro tema importante de esta tesis ha sido los métodos de preservación de la privacidad. Hemos presentado nuestro algoritmo de base aleatoria, que utiliza el concepto de Edge neighbourhood centrality para guiar el proceso de modificación preservando los bordes más importantes del grafo, logrando una menor pérdida de información y una mayor utilidad de los datos. Por último, se han desarrollado dos algoritmos diferentes para el k-anonimato en los grafos. En primer lugar, se ha presentado un algoritmo basado en la computación evolutiva. Aunque este método nos permite cumplir el nivel de privacidad deseado, presenta dos inconvenientes: la pérdida de información es bastante grande en algunas propiedades estructurales del grafo y no es lo suficientemente rápido para trabajar con grandes redes. Por lo tanto, un segundo algoritmo se ha presentado, que utiliza el micro-agregación univariante para anonimizar la secuencia de grados. Este método es cuasi-óptimo y se traduce en una menor pérdida de información y una mejor utilidad de los datos.
In recent years, an explosive increase of graph-formatted data has been made publicly available. Embedded within this data there is private information about users who appear in it. Therefore, data owners must respect the privacy of users before releasing datasets to third parties. In this scenario, anonymization processes become an important concern. However, anonymization processes usually introduce some kind of noise in the anonymous data, altering the data and also their results on graph mining processes. Generally, the higher the privacy, the larger the noise. Thus, data utility is an important factor to consider in anonymization processes. The necessary trade-off between data privacy and data utility can be reached by using measures and metrics to lead the anonymization process to minimize the information loss, and therefore, to maximize the data utility. In this thesis we have covered the fields of user's privacy-preserving in social networks and the utility and quality of the released data. A trade-off between both fields is a critical point to achieve good anonymization methods for the subsequent graph mining processes. Part of this thesis has focused on data utility and information loss. Firstly, we have studied the relation between the generic information loss measures and the clustering-specific ones, in order to evaluate whether the generic information loss measures are indicative of the usefulness of the data for subsequent data mining processes. We have found strong correlation between some generic information loss measures (average distance, betweenness centrality, closeness centrality, edge intersection, clustering coefficient and transitivity) and the precision index over the results of several clustering algorithms, demonstrating that these measures are able to predict the perturbation introduced in anonymous data. Secondly, two measures to reduce the information loss on graph modification processes have been presented. The first one, Edge neighbourhood centrality, is based on information flow throw 1-neighbourhood of a specific edge in the graph. The second one is based on the core number sequence and it preserves better the underlying graph structure, retaining more data utility. By an extensive experimental set up, we have demonstrated that both methods are able to preserve the most important edges in the network, keeping the basic structural and spectral properties close to the original ones. The other important topic of this thesis has been privacy-preserving methods. We have presented our random-based algorithm, which utilizes the concept of Edge neighbourhood centrality to drive the edge modification process to better preserve the most important edges in the graph, achieving lower information loss and higher data utility on the released data. Our method obtains a better trade-off between data utility and data privacy than other methods. Finally, two different approaches for k-degree anonymity on graphs have been developed. First, an algorithm based on evolutionary computing has been presented and tested on different small and medium real networks. Although this method allows us to fulfil the desired privacy level, it presents two main drawbacks: the information loss is quite large in some graph structural properties and it is not fast enough to work with large networks. Therefore, a second algorithm has been presented, which uses the univariate micro-aggregation to anonymize the degree sequence and reduce the distance from the original one. This method is quasi-optimal and it results in lower information loss and better data utility.

La science moderne des graphes est apparue ces dernières années comme un domaine d'intérêt et a apporté des progrès significatifs à notre connaissance des réseaux. Jusqu'à récemment, les algorithmes d'exploration de données existants étaient destinés à des données structurées / relationnelles, alors que de nombreux ensembles de données nécessitent une représentation graphique, comme les réseaux sociaux, les réseaux générés par des données textuelles, les structures protéiques 3D ou encore les composés chimiques. Il est donc crucial de pouvoir extraire des informations pertinantes à partir de ce type de données et, pour ce faire, les méthodes d'extraction et d'analyse des graphiques ont été prouvées essentielles.L'objectif de cette thèse est d'étudier les problèmes dans le domaine de la fouille de graphes axés en particulier sur la conception de nouveaux algorithmes et d'outils liés à la diffusion d'informations et plus spécifiquement sur la façon de localiser des entités influentes dans des réseaux réels. Cette tâche est cruciale dans de nombreuses applications telles que la diffusion de l'information, les contrôles épidémiologiques et le marketing viral.Dans la première partie de la thèse, nous avons étudié les processus de diffusion dans les réseaux sociaux ciblant la recherche de caractéristiques topologiques classant les entités du réseau en fonction de leurs capacités influentes. Nous nous sommes spécifiquement concentrés sur la décomposition K-truss qui est une extension de la décomposition k-core. On a montré que les noeuds qui appartiennent au sous-graphe induit par le maximal K-truss présenteront de meilleurs proprietés de propagation par rapport aux critères de référence. De tels épandeurs ont la capacité non seulement d'influencer une plus grande partie du réseau au cours des premières étapes d'un processus d'étalement, mais aussi de contaminer une plus grande partie des noeuds.Dans la deuxième partie de la thèse, nous nous sommes concentrés sur l'identification d'un groupe de noeuds qui, en agissant ensemble, maximisent le nombre attendu de nœuds influencés à la fin du processus de propagation, formellement appelé Influence Maximization (IM). Le problème IM étant NP-hard, il existe des algorithmes efficaces garantissant l’approximation de ses solutions. Comme ces garanties proposent une approximation gloutonne qui est coûteuse en termes de temps de calcul, nous avons proposé l'algorithme MATI qui réussit à localiser le groupe d'utilisateurs qui maximise l'influence, tout en étant évolutif. L'algorithme profite des chemins possibles créés dans le voisinage de chaque nœud et précalcule l'influence potentielle de chaque nœud permettant ainsi de produire des résultats concurrentiels, comparés à ceux des algorithmes classiques.Finallement, nous étudions le point de vue de la confidentialité quant au partage de ces bons indicateurs d’influence dans un réseau social. Nous nous sommes concentrés sur la conception d'un algorithme efficace, correct, sécurisé et de protection de la vie privée, qui résout le problème du calcul de la métrique k-core qui mesure l'influence de chaque noeud du réseau. Nous avons spécifiquement adopté une approche de décentralisation dans laquelle le réseau social est considéré comme un système Peer-to-peer (P2P). L'algorithme est construit de telle sorte qu'il ne devrait pas être possible pour un nœud de reconstituer partiellement ou entièrement le graphe en utilisant les informations obtiennues lors de son exécution. Notre contribution est un algorithme incrémental qui résout efficacement le problème de maintenance de core en P2P tout en limitant le nombre de messages échangés et les calculs. Nous fournissons également une étude de sécurité et de confidentialité de la solution concernant la désanonymisation des réseaux, nous montrons ainsi la rélation avec les strategies d’attaque précédemment definies tout en discutant les contres-mesures adaptés
Modern science of graphs has emerged the last few years as a field of interest and has been bringing significant advances to our knowledge about networks. Until recently the existing data mining algorithms were destined for structured/relational data while many datasets exist that require graph representation such as social networks, networks generated by textual data, 3D protein structures and chemical compounds. It has become therefore of crucial importance to be able to extract meaningful information from that kind of data and towards this end graph mining and analysis methods have been proven essential. The goal of this thesis is to study problems in the area of graph mining focusing especially on designing new algorithms and tools related to information spreading and specifically on how to locate influential entities in real-world networks. This task is crucial in many applications such as information diffusion, epidemic control and viral marketing. In the first part of the thesis, we have studied spreading processes in social networks focusing on finding topological characteristics that rank entities in the network based on their influential capabilities. We have specifically focused on the K-truss decomposition which is an extension of the core decomposition of the graph. Extensive experimental analysis showed that the nodes that belong to the maximal K-truss subgraph show a better spreading behavior when compared to baseline criteria. Such spreaders can influence a greater part of the network during the first steps of a spreading process but also the total fraction of the influenced nodes at the end of the epidemic is greater. We have also observed that node members of such dense subgraphs are those achieving the optimal spreading in the network.In the second part of the thesis, we focused on identifying a group of nodes that by acting all together maximize the expected number of influenced nodes at the end of the spreading process, formally called Influence Maximization (IM). The IM problem is actually NP-hard though there exist approximation guarantees for efficient algorithms that can solve the problem while obtaining a solution within the 63% of optimal classes of models. As those guarantees propose a greedy approximation which is computationally expensive especially for large graphs, we proposed the MATI algorithm which succeeds in locating the group of users that maximize the influence while also being scalable. The algorithm takes advantage the possible paths created in each node’s neighborhood to precalculate each node’s potential influence and produces competitive results in quality compared to those of baseline algorithms such as the Greedy, LDAG and SimPath. In the last part of the thesis, we study the privacy point of view of sharing such metrics that are good influential indicators in a social network. We have focused on designing an algorithm that addresses the problem of computing through an efficient, correct, secure, and privacy-preserving algorithm the k-core metric which measures the influence of each node of the network. We have specifically adopted a decentralization approach where the social network is considered as a Peer-to-peer (P2P) system. The algorithm is built based on the constraint that it should not be possible for a node to reconstruct partially or entirely the graph using the information they obtain during its execution. While a distributed algorithm that computes the nodes’ coreness is already proposed, dynamic networks are not taken into account. Our main contribution is an incremental algorithm that efficiently solves the core maintenance problem in P2P while limiting the number of messages exchanged and computations. We provide a security and privacy analysis of the solution regarding network de-anonimization and show how it relates to previously defined attacks models and discuss countermeasures

In recent years we have seen an increase in the number of public transit service disruptions due to aging infrastructure, system failures and the regular need for maintenance. With the fleeting growth in the usage of these transit networks there has been an increase in the need for the timely detection of such disruptions. Any types of disruptions in these transit networks can lead to delays which can have major implications on the daily passengers. Most current disruption detection systems either do not operate in real-time or lack transit network coverage. The theme of this thesis was to leverage Twitter data to help in earlier detection of service disruptions. This work involves developing a pure Data Mining approach and a couple different approaches that use Graph Neural Networks to identify transit disruption related information in Tweets from a live Twitter stream related to the Washington Metropolitan Area Transit Authority (WMATA) metro system. After developing three different models, a Dynamic Query Expansion model, a Tweet-GCN and a Tweet-Level GCN to represent the data corpus we performed various experiments and benchmark evaluations against other existing baseline models, to justify the efficacy of our approaches. After seeing astounding results across both the Tweet-GCN and Tweet-Level GCN, with an average accuracy of approximately 87.3% and 89.9% we can conclude that not only are these two graph neural models superior for basic NLP text classification, but they also outperform other models in identifying transit disruptions.
Master of Science
Millions of people worldwide rely on public transit networks for their daily commutes and day to day movements. With the growth in the number of people using the service, there has been an increase in the number of daily passengers affected by service disruptions. This thesis and research involves proposing and developing three different approaches to help aid in the timely detection of these disruptions. In this work we have developed a pure data mining approach along with two deep learning models using neural networks and live data from Twitter to identify these disruptions. The data mining approach uses a set of dirsuption related input keywords to identify similar keywords within the live Twitter data. By collecting historical data we were able to create deep learning models that represent the vocabulary from the disruptions related Tweets in the form of a graph. A graph is a collection of data values where the data points are connected to one another based on their relationships. A longer chain of connection between two words defines a weak relationship, a shorter chain defines a stronger relationship. In our graph, words with similar contextual meanings are connected to each other over shorter distances, compared to words with different meanings. At the end we use a neural network as a classifier to scan this graph to learn the semantic relationships within our data. Afterwards, this learned information can be used to accurately classify the disruption related Tweets within a pool of random Tweets. Once all the proposed approaches have been developed, a benchmark evaluation is performed against other existing text classification techniques, to justify the effectiveness of the approaches. The final results indicate that the proposed graph based models achieved a higher accuracy, compared to the data mining model, and also outperformed all the other baseline models. Our Tweet-Level GCN had the highest accuracy of 89.9%.

With the growing spread of spatial data, exploratory analysis has gained a considerable amount of attention. Particularly in the fields of Information Retrieval and Data Mining, the integration of data points helps uncover interesting patterns not always visible to the naked eye. Social networks often link entities that share places and activities; marketing tools target users based on behavior and preferences; and medical technology combines symptoms to categorize diseases. Many of the current approaches in this field of research depend on semantic analysis, which is good for inferencing and decision making. From a functional point of view, objects can be investigated from a spatial and temporal perspectives. The former attempts to verify how proximity makes the objects related; the latter adds a measure of coherence by enforcing time ordering. This type of spatio-temporal reasoning examines several aspects of semantic analysis and their characteristics: shared relationships among objects, matches versus mismatches of values, distances among parents and children, and bruteforce comparison of attributes. Most of these approaches suffer from the pitfalls of disparate data, often missing true relationships, failing to deal with inexact vocabularies, ignoring missing values, and poorly handling multiple attributes. In addition, the vast majority does not consider the spatio-temporal aspects of the data. This research studies semantic techniques of data analysis in spatial contexts. The proposed solutions represent different methods on how to relate spatial entities or sequences of entities. They are able to identify relationships that are not explicitly written down. Major contributions of this research include (1) a framework that computes a numerical entity similarity, denoted a semantic footprint, composed of spatial, dimensional, and ontological facets; (2) a semantic approach that translates categorical data into a numerical score, which permits ranking and ordering; (3) an extensive study of GML as a representative spatial structure of how semantic analysis methods are influenced by its approaches to storage, querying, and parsing; (4) a method to find spatial regions of high entity density based on a clustering coefficient; (5) a ranking strategy based on connectivity strength which differentiates important relationships from less relevant ones; (6) a distance measure between entity sequences that quantifies the most related streams of information; (7) three distance-based measures (one probabilistic, one based on spatial influence, and one that is spatiological) that quantifies the interactions among entities and events; (8) a spatio-temporal method to compute the coherence of a data sequence.
Ph. D.

Rumor spreaders are increasingly taking advantage of multimedia content to attract and mislead news consumers on social media. Although recent multimedia rumor detection models have exploited both textual and visual features for classification, they do not integrate the social structure features simultaneously, which have shown promising performance for rumor identification. It is challenging to combine the heterogeneous multi-modal data in consideration of their complex relationships. In this work, we propose a novel Multi-modal Feature-enhanced Attention Networks (MFAN) for rumor detection, which makes the first attempt to integrate textual, visual, and social graph features in one unified framework. Specifically, it considers both the complement and alignment relationships between different modalities to achieve better fusion. Moreover, it takes into account the incomplete links in the social network data due to data collection constraints and proposes to infer hidden links to learn better social graph features. The experimental results show that MFAN can detect rumors effectively and outperform state-of-the-art methods.

Technologies are increasingly taking advantage of the explosion in the amount of data generated by social multimedia (e.g., web searches, ad targeting, and urban computing). In this paper, we propose a multi-view learning framework for presenting the construction of a new urban movement knowledge graph, which could greatly facilitate the research domains mentioned above. In particular, by viewing GPS trajectory data from temporal, spatial, and spatiotemporal points of view, we construct a knowledge graph of which nodes and edges are their locations and relations, respectively. On the knowledge graph, both nodes and edges are represented in latent semantic space. We verify its utility by subsequently applying the knowledge graph to predict the extent of user attention (high or low) paid to different locations in a city. Experimental evaluations and analysis of a real-world dataset show significant improvements in comparison to state-of-the-art methods.

The publication explores the features of the digital technologies’ usage to organize the work of the Young Scientists’ Councils and describes the best practices. The digital transformation of society and the quarantine restrictions caused by the COVID-19 pandemic have forced the use of various digital technologies for scientific communication, the organization of work for youth associations, and the training of students and Ph.D. students. An important role in increasing the prestige of scientific activity and encouraging talented young people to participate in scientific projects belongs to the Young Scientists’ Councils, which are created at scientific institutions and higher education institutions. It is determined that the peculiarities of the work of Young Scientists’ Councils are in providing conditions for further staff development of the institution in which they operate; contribution to the social, psychological and material support of young scientists and Ph.D. students; creating an environment for teamwork and collaborative partnership; development of leadership and organizational qualities; contribution to the development of digital competence. The advantages of using electronic social networks in higher education and research institutions are analyzed, namely: general popularity and free of charge; prompt exchange of messages and multimedia data; user-friendly interface; availability of event planning functions, sending invitations, setting reminders; support of synchronous and asynchronous communication between network participants; possibility of access from various devices; a powerful tool for organizing the learning process; possibility of organization and work of closed and open groups; advertising of various events, etc. Peculiarities of managing the activity of the Young Scientists’ Council with the use of digital technologies are determined. The Young Scientists’ Council is a social system, and therefore the management of this system refers to social management. The effectiveness of the digital technologies’ usage to manage the activities of the Young Scientists’ Council depends on the intensity and need for their use to implement organizational, presentation functions and to ensure constant communication. The areas to apply digital technologies for the work managing of Young Scientists’ Councils are sorted as the presentation of activity; distribution of various information for young scientists; conducting questionnaires, surveys; organization and holding of scientific mass events; managing of thematic workgroups, holding of work meetings. It is generalized and described the experience of electronic social networks usage for organizing and conducting of scientific mass events.

Recent data suggest that the technical staff’s appraisals of the condition and development of economic activity, inflation and the labor market have been in line with current trends, marked by a decline in demand and the persistence of ample excess productive capacity. A significant projected fall in output materialized in the second quarter, contributing to a decline in inflation below the 3% target and reflected in a significant deterioration of the labor market. A slow recovery in output and employment is expected to continue for the remainder of 2020 and into next year, alongside growing inflation that should remain below the target. The Colombian economy is likely to undergo a significant recession in 2020 (GDP contraction of 7.6%), though this may be less severe than projected in the previous report (-8.5%). Output is expected to have begun a slow recovery in the second half of this year, though it is not projected to return to pre-pandemic levels in 2021 amid significant global uncertainty. The output decline in the first half of 2020 was less severe than anticipated, thanks to an upward revision in first-quarter GDP and a smaller contraction in the second quarter (-15.5%) than had been projected (-16.5%). Available economic indicators suggest an annual decline in GDP in the third quarter of around 9%. No significant acceleration of COVID-19 cases that would imply a tightening of social distancing measures is presumed for the remainder of this year or in 2021. In that context, a gradual opening of the economy would be expected to continue, with supply in sectors that have been most affected by the pandemic recovering slowly as restrictions on economic activity continue to be relaxed. On the spending side, an improvement in consumer confidence, suppressed demand for goods and services, low interest rates, and higher expected levels of foreign demand should contribute to a recovery in output. A low base of comparison would also help explain the expected increase in GDP in 2021. Based on the conditions laid out above, economic growth in 2020 is expected to be between -9% and -6.5%, with a central value of -7.6%. Growth in 2021 is projected to be between 3% and 7%, with a central value of 4.6% (Graph 1.1). Upward revisions compared to the July report take into account a lower-than-expected fall in first-semester growth and a somewhat faster recovery in the third quarter in some sectors. The forecast intervals for 2020 and 2021 growth tightened somewhat but continue to reflect a high degree of uncertainty over theevolution of the pandemic, the easures required to deal with it, and their effects on global and domestic economic activity.

Macroeconomic summary Several factors contributed to an increase in projected inflation on the forecast horizon, keeping it above the target rate. These included inflation in December that surpassed expectations (5.62%), indexation to higher inflation rates for various baskets in the consumer price index (CPI), a significant real increase in the legal minimum wage, persistent external and domestic inflationary supply shocks, and heightened exchange rate pressures. The CPI for foods was affected by the persistence of external and domestic supply shocks and was the most significant contributor to unexpectedly high inflation in the fourth quarter. Price adjustments for fuels and certain utilities can explain the acceleration in inflation for regulated items, which was more significant than anticipated. Prices in the CPI for goods excluding food and regulated items also rose more than expected. This was partly due to a smaller effect on prices from the national government’s VAT-free day than anticipated by the technical staff and more persistent external pressures, including via peso depreciation. By contrast, the CPI for services excluding food and regulated items accelerated less than expected, partly reflecting strong competition in the communications sector. This was the only major CPI basket for which prices increased below the target inflation rate. The technical staff revised its inflation forecast upward in response to certain external shocks (prices, costs, and depreciation) and domestic shocks (e.g., on meat products) that were stronger and more persistent than anticipated in the previous report. Observed inflation and a real increase in the legal minimum wage also exceeded expectations, which would boost inflation by affecting price indexation, labor costs, and inflation expectations. The technical staff now expects year-end headline inflation of 4.3% in 2022 and 3.4% in 2023; core inflation is projected to be 4.5% and 3.6%, respectively. These forecasts consider the lapse of certain price relief measures associated with the COVID-19 health emergency, which would contribute to temporarily keeping inflation above the target on the forecast horizon. It is important to note that these estimates continue to contain a significant degree of uncertainty, mainly related to the development of external and domestic supply shocks and their ultimate effects on prices. Other contributing factors include high price volatility and measurement uncertainty related to the extension of Colombia’s health emergency and tax relief measures (such as the VAT-free days) associated with the Social Investment Law (Ley de Inversión Social). The as-yet uncertain magnitude of the effects of a recent real increase in the legal minimum wage (that was high by historical standards) and high observed and expected inflation, are additional factors weighing on the overall uncertainty of the estimates in this report. The size of excess productive capacity remaining in the economy and the degree to which it is closing are also uncertain, as the evolution of the pandemic continues to represent a significant forecast risk. margin, could be less dynamic than expected. And the normalization of monetary policy in the United States could come more quickly than projected in this report, which could negatively affect international financing costs. Finally, there remains a significant degree of uncertainty related to the duration of supply chocks and the degree to which macroeconomic and political conditions could negatively affect the recovery in investment. The technical staff revised its GDP growth projection for 2022 from 4.7% to 4.3% (Graph 1.3). This revision accounts for the likelihood that a larger portion of the recent positive dynamic in private consumption would be transitory than previously expected. This estimate also contemplates less dynamic investment behavior than forecast in the previous report amid less favorable financial conditions and a highly uncertain investment environment. Third-quarter GDP growth (12.9%), which was similar to projections from the October report, and the fourth-quarter growth forecast (8.7%) reflect a positive consumption trend, which has been revised upward. This dynamic has been driven by both public and private spending. Investment growth, meanwhile, has been weaker than forecast. Available fourth-quarter data suggest that consumption spending for the period would have exceeded estimates from October, thanks to three consecutive months that included VAT-free days, a relatively low COVID-19 caseload, and mobility indicators similar to their pre-pandemic levels. By contrast, the most recently available figures on new housing developments and machinery and equipment imports suggest that investment, while continuing to rise, is growing at a slower rate than anticipated in the previous report. The trade deficit is expected to have widened, as imports would have grown at a high level and outpaced exports. Given the above, the technical staff now expects fourth-quarter economic growth of 8.7%, with overall growth for 2021 of 9.9%. Several factors should continue to contribute to output recovery in 2022, though some of these may be less significant than previously forecast. International financial conditions are expected to be less favorable, though external demand should continue to recover and terms of trade continue to increase amid higher projected oil prices. Lower unemployment rates and subsequent positive effects on household income, despite increased inflation, would also boost output recovery, as would progress in the national vaccination campaign. The technical staff expects that the conditions that have favored recent high levels of consumption would be, in large part, transitory. Consumption spending is expected to grow at a slower rate in 2022. Gross fixed capital formation (GFCF) would continue to recover, approaching its pre-pandemic level, though at a slower rate than anticipated in the previous report. This would be due to lower observed GFCF levels and the potential impact of political and fiscal uncertainty. Meanwhile, the policy interest rate would be less expansionary as the process of monetary policy normalization continues. Given the above, growth in 2022 is forecast to decelerate to 4.3% (previously 4.7%). In 2023, that figure (3.1%) is projected to converge to levels closer to the potential growth rate. In this case, excess productive capacity would be expected to tighten at a similar rate as projected in the previous report. The trade deficit would tighten more than previously projected on the forecast horizon, due to expectations of an improved export dynamic and moderation in imports. The growth forecast for 2022 considers a low basis of comparison from the first half of 2021. However, there remain significant downside risks to this forecast. The current projection does not, for example, account for any additional effects on economic activity resulting from further waves of COVID-19. High private consumption levels, which have already surpassed pre-pandemic levels by a large margin, could be less dynamic than expected. And the normalization of monetary policy in the United States could come more quickly than projected in this report, which could negatively affect international financing costs. Finally, there remains a significant degree of uncertainty related to the duration of supply chocks and the degree to which macroeconomic and political conditions could negatively affect the recovery in investment. External demand for Colombian goods and services should continue to recover amid significant global inflation pressures, high oil prices, and less favorable international financial conditions than those estimated in October. Economic activity among Colombia’s major trade partners recovered in 2021 amid countries reopening and ample international liquidity. However, that growth has been somewhat restricted by global supply chain disruptions and new outbreaks of COVID-19. The technical staff has revised its growth forecast for Colombia’s main trade partners from 6.3% to 6.9% for 2021, and from 3.4% to 3.3% for 2022; trade partner economies are expected to grow 2.6% in 2023. Colombia’s annual terms of trade increased in 2021, largely on higher oil, coffee, and coal prices. This improvement came despite increased prices for goods and services imports. The expected oil price trajectory has been revised upward, partly to supply restrictions and lagging investment in the sector that would offset reduced growth forecasts in some major economies. Elevated freight and raw materials costs and supply chain disruptions continue to affect global goods production, and have led to increases in global prices. Coupled with the recovery in global demand, this has put upward pressure on external inflation. Several emerging market economies have continued to normalize monetary policy in this context. Meanwhile, in the United States, the Federal Reserve has anticipated an end to its asset buying program. U.S. inflation in December (7.0%) was again surprisingly high and market average inflation forecasts for 2022 have increased. The Fed is expected to increase its policy rate during the first quarter of 2022, with quarterly increases anticipated over the rest of the year. For its part, Colombia’s sovereign risk premium has increased and is forecast to remain on a higher path, to levels above the 15-year-average, on the forecast horizon. This would be partly due to the effects of a less expansionary monetary policy in the United States and the accumulation of macroeconomic imbalances in Colombia. Given the above, international financial conditions are projected to be less favorable than anticipated in the October report. The increase in Colombia’s external financing costs could be more significant if upward pressures on inflation in the United States persist and monetary policy is normalized more quickly than contemplated in this report. As detailed in Section 2.3, uncertainty surrounding international financial conditions continues to be unusually high. Along with other considerations, recent concerns over the potential effects of new COVID-19 variants, the persistence of global supply chain disruptions, energy crises in certain countries, growing geopolitical tensions, and a more significant deceleration in China are all factors underlying this uncertainty. The changing macroeconomic environment toward greater inflation and unanchoring risks on inflation expectations imply a reduction in the space available for monetary policy stimulus. Recovery in domestic demand and a reduction in excess productive capacity have come in line with the technical staff’s expectations from the October report. Some upside risks to inflation have materialized, while medium-term inflation expectations have increased and are above the 3% target. Monetary policy remains expansionary. Significant global inflationary pressures and the unexpected increase in the CPI in December point to more persistent effects from recent supply shocks. Core inflation is trending upward, but remains below the 3% target. Headline and core inflation projections have increased on the forecast horizon and are above the target rate through the end of 2023. Meanwhile, the expected dynamism of domestic demand would be in line with low levels of excess productive capacity. An accumulation of macroeconomic imbalances in Colombia and the increased likelihood of a faster normalization of monetary policy in the United States would put upward pressure on sovereign risk perceptions in a more persistent manner, with implications for the exchange rate and the natural rate of interest. Persistent disruptions to international supply chains, a high real increase in the legal minimum wage, and the indexation of various baskets in the CPI to higher inflation rates could affect price expectations and push inflation above the target more persistently. These factors suggest that the space to maintain monetary stimulus has continued to diminish, though monetary policy remains expansionary. 1.2 Monetary policy decision Banco de la República’s board of directors (BDBR) in its meetings in December 2021 and January 2022 voted to continue normalizing monetary policy. The BDBR voted by a majority in these two meetings to increase the benchmark interest rate by 50 and 100 basis points, respectively, bringing the policy rate to 4.0%.