Dissertations / Theses on the topic 'Graph, social and multimedia data'
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Kim, Pilho. "E-model event-based graph data model theory and implementation /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29608.
Full textCommittee 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.
Wang, Guan. "Graph-Based Approach on Social Data Mining." Thesis, University of Illinois at Chicago, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668648.
Full textPowered 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.
Wong, León Kevin, and Valdivia Diego Eduardo Antonio Rodríguez. "Distributed Social Media System - Multimedia Data Linkage." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2014. http://hdl.handle.net/10757/324525.
Full textTesis
Bracamonte, Nole Teresa Jacqueline. "Improving web multimedia information retrieval using social data." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168681.
Full textBuscar 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
Hassanzadeh, Reza. "Anomaly detection in online social networks : using data-mining techniques and fuzzy logic." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/78679/1/Reza_Hassanzadeh_Thesis.pdf.
Full textMaryokhin, Tymur. "Data dissemination in large-cardinality social graphs." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-48268.
Full textCasas, Roma Jordi. "Privacy-preserving and data utility in graph mining." Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/285566.
Full textIn 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.
Rossi, Maria. "Graph Mining for Influence Maximization in Social Networks." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX083/document.
Full textModern 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
Zulfiqar, Omer. "Detecting Public Transit Service Disruptions Using Social Media Mining and Graph Convolution." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103745.
Full textMaster 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%.
Dos, Santos Raimundo Fonseca Jr. "Effective Methods of Semantic Analysis in Spatial Contexts." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/49697.
Full textPh. D.
Cimenler, Oguz. "Social Network Analysis of Researchers' Communication and Collaborative Networks Using Self-reported Data." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5201.
Full textFang, Chunsheng. "Novel Frameworks for Mining Heterogeneous and Dynamic Networks." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978.
Full textRuan, Yiye. "Joint Dynamic Online Social Network Analytics Using Network, Content and User Characteristics." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420765022.
Full textGreen, Oded. "High performance computing for irregular algorithms and applications with an emphasis on big data analytics." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51860.
Full textAnderson, Paul. "GeoS: A Service for the Management of Geo-Social Information in a Distributed System." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1561.
Full textGiannini, Andrea. "Social Network Analysis: Architettura Streaming Big Data di Raccolta e Analisi Dati da Twitter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25378/.
Full textCharbey, Raphaël. "Sociabilités en ligne, usages et réseaux." Thesis, Paris, ENST, 2018. http://www.theses.fr/2018ENST0049/document.
Full textWith the digital advent, it is now possible for researchers to collect important amounts of data and online social network platforms are surely part of it. Sociologists, among others, seized those new resources to investigate over interaction modalities between individuals as well as their impact on the structure of sociability. Following this lead, this thesis work aims at analyzing a large number of Facebook accounts, through data analysis and graph theory classical tools, and to bring methodological contributions. Two main factors encourage to study Facebook social activities. On one hand, the importance of time spent on this platform by many Internet users justifies by itself the sociologists interest. On the other, and contrarily to what we observe on other social network websites, ties between individuals are similar to the ones that appear offline. First, the thesis proposes to detangle the multiple meanings that are behind the fact of ”being on Facebook”. The uses of our surveyed are not compacted in fantasized normative practices but vary depending on how they appropriate the different composers of the platform tools. These uses, as we will see it, do not concern all the socioprofessional categories in the same way and they also influence how the respondents interact with their online friends. The manuscript also explores these interactions, as well as the lover role into the relational structure. Second part of the thesis builds a typology of these relational structures. They are said as egocentred, which means that they are taken from the perspective of the respondent. This typology of social networks is based on their graphlet counts, that are the number of times each type of subnetwork appear in them. This approach offers a meso perspective (between micro and macro), that is propitious to underline some new social phenomena. With a high pluri-disciplinary potential, the graphlet methodology is also discussed and explored itself
Gabardo, Ademir cristiano. "A heuristic to detect community structures in dynamic complex networks." Universidade Tecnológica Federal do Paraná, 2014. http://repositorio.utfpr.edu.br/jspui/handle/1/970.
Full textKourtellis, Nicolas. "On the Design of Socially-Aware Distributed Systems." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4107.
Full textFleig, John David. "Citationally Enhanced Semantic Literature Based Discovery." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1082.
Full textGilbert, Frédéric. "Méthodes et modèles pour la visualisation de grandes masses de données multidimensionnelles nominatives dynamiques." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14498/document.
Full textSince ten years, informations visualization domain knows a real interest.Recently, with the growing of communications, the research on social networks analysis becomes strongly active. In this thesis, we present results on dynamic social networks analysis. That means that we take into account the temporal aspect of data. We were particularly interested in communities extraction within networks and their evolutions through time. [...]
Novosad, Andrej. "Využití metod dolování dat pro analýzu sociálních sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236424.
Full textHsieh, Liang-Chi, and 謝良奇. "Image Graph Construction and Semantic Annotation for Large-Scale Social Multimedia." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/90088768166155247056.
Full text國立臺灣大學
資訊網路與多媒體研究所
102
In recent years, mobile devices equipped with cameras prevail on consumer markets. These devices plus the emerged trend of multimedia sharing on social networks, makes the scale of multimedia data grow explosively. These raw multimedia data are usually stored without well organized. That causes significant challenge to further retrieving and using these content. With regard to the large-scale multimedia content, we can explore and leverage hidden relations and semantic meanings to help us create useful multimedia applications. In this dissertation, we focus on two problems faced in dealing with large-scale multimedia: data volume and semantics. First, for the data volume problem, in order to improve navigation and search experience over large-scale image data, we investigate the efficient method to construct image graphs that represent visual and semantic relations between images. We leverage constructed graphs to build efficient and scalable group-based image search system. Binary codes are very compact representation for storing and searching image data. However, how to efficient index and search very large-scale images encoded as longer binary codes is still a challenging problem. We propose a new search framework for very large-scale binary image codes that leverages GPU devices to achieve better performance and storage efficiency than previous works. For the second problem with regard to multimedia semantics, we propose several methods to extract semantics from multimedia content shared in social networks. There exist bother visual and semantic relations between images. These relations can be explored to help us better navigate and use image collections. However, current image search systems generally use multi-pages image list to display their search results. The list causes no significant harm when the user''s search target is obvious. However, in the case with the query of higher ambiguity, it is usually difficult for users to find their search targets in such long image list. The kind of paged image lists causes browsing problem for mobile devices too. That is because mobile devices are usually only equipped display screen with limited size. Thus, we propose to build a group-based image search system that summarizes image search results in semantic and visual groups. We leverage visual and semantic relations of images to construct image graphs at offline stage. This design makes the system be efficient at responding user online query. In order to scale up for large-scale images, we propose to use modern parallel technology MapReduce to solve scalability issue in this system. Compared with constructing graphs on single machine, our graph construction method is 69 times faster. In order to solve the data volume problem faced by processing very large-scale image data, binary codes are recently recognized as enabling and promising technique for encoding and searching images. The compact representation of binary code provides better storage efficiency when dealing with huge image data. Besides, compared with other image representations, the pairwise similarity computation of binary codes is much faster. For example, the similarity comparison between a query and millions of binary codes can be done in less than one second with very simple baseline method of linear scanning. These advantages make binary codes as an important component for applications on very large-scale image data. However, when it is required to encode very large-scale image data (at least 1 billion images) as longer binary codes (more than 32 bits), how to efficiently store and search these binary codes still is a challenging problem. We propose a new framework to store and search very large-scale binary codes that leverages GPU devices. Compared with multiple hashing index method proposed in previous work, our random-sampling index approaches are more storage efficient and simpler. It supports both exact and approximate nearest neighbor search on binary codes. By leveraging the parallel computation of GPU, we also achieve faster search time performance than previous works. In order to further improve storage efficiency of our index, we propose a compression scheme for binary codes called bit compression. With GPU-based decompression method, compression version of index would not sacrifice too much search performance. Large-scale image data without properly annotated hinders image browsing and searching application. This problem motivates the development of effective automatic image annotation method. Given an image without textual information, automatic image annotation method can select best textual annotations for the image. Prior works in this area mostly focus on supervised learning approaches. These approaches are not practical due to poor performance, out-of-vocabulary problem, and being time-consuming in acquiring training data and learning. Thus, we claim that automatic image annotation by search over user-contributed photo sites (e.g., Flickr) would be an alternative solution to this problem. The intuition behind it is to select most suitable annotations for unlabeled image from the tags associated with visually similar user-contributed photos. However, the tags are generally few and noisy. To solve this problem, we propose a tag expansion method and use visual and semantic consistency between tag and image. We show that the proposed method significantly outperforms prior works and even provide more diverse annotations. Microblogging as a new form of communication on Internet, has attracted the attention from researchers recently. Relying the real-time and conversational properties of microblogging, its users update their statuses and share experience within their the social network. Those characteristics also make microblogging an important tool for users to share or discuss real world events such as earth quake or sport game. We propose a novel and flexible solution to detect and recognize real-time events from sport games based on analyzing the messages posted on microblogging services. We take Twitter as the experiment platform and collect a large-scale dataset of Twitter messages that are called tweets for 18 prominent sport games covering four types of sports in 2011. We also collect corresponding sport videos for those games. The proposed solution applies moving-threshold burst detection on the volume of tweets to detect highlights in sport games. A tf-idf-based weighting method is applied on the tweets within detected highlights for semantic extraction. According to the experiments we perform on the tweet and video datasets, we find that the proposed methods can achieve competent performance in sport event detection and recognition. Besides, our method can find non pre-defined tidbits that are difficult to detect in previous works. Not all images are interesting to people. People are drawn by interesting images and ignore tasteless ones. Image interestingness has the importance no less than other subjective image properties that have received significant research interest, but has not been systematically studied before. In this proposal, we focus on visual and social aspects of image interestingness. We rely on crowdsourcing tools to survey human perceptions for these subjective properties and verify data by analyzing consistency and reliability. We show that people have an agreement when deciding if an image is interesting or not. We examine the correlation between the social, visual aspects of interestingness and aesthetics. By exploring the correlation, we find that: (1) Weak correlation between social interestingness and both of visual interestingness and image aesthetics indicates that the images frequently re-shared by people are not necessarily aesthetic or visually interesting. (2) High correlation between image aesthetics and visual interestingness implies aesthetic images are more likely to be visually interesting to people. Then we wonder what features of an image lead to social interestingness, e.g. receiving more likes and shares on social networking sites? We train classifiers to predict visual and social interestingness and investigate the contribution from different image features. We find that social and visual interestingness can be best predicted with color and texture, respectively, providing a way to manipulate social and visual liking of images with image features. Further, we investigate the correlation between social/visual image interestingness and image color. We find that colors with arousal effect show more frequently in images with higher social interestingness. That could be explained by previous studies for activation-related affect of colors and provides useful and important advice when advertising on social networking sites.
Tai, Chih-Hua, and 戴志華. "Graph-based Data Mining for Transactional,Spatial and Social-networking Data." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/02698742304930048631.
Full text國立臺灣大學
電機工程學研究所
99
Data Mining is a data-and-application dependant technique, and has received significant attentions in the last decade. In the past years, various techniques have been developed to deal with set or sequence data in business marketing, computer networks, bioinformatics, to name a few. Many real applications, however, have called for the need of new techniques to tackle data with structural information, i.e., graphs. Graph-based data mining, which discovers novel knowledge in graph-represented data, is thus becoming more and more important. In this dissertation, motivated by the fact that graph-based data mining is still in its fancy compared to the wide applications, we attempt to address the use of graph-based data mining in realistic problems with three kinds of data complexity, respectively. First, due to the rise of cloud computing, people who lack of expertise in data mining and/or computational resources now can also take advantages from data mining by outsourcing their mining tasks. However, for any outsourcing service, privacy is a major concern. In Chapter 2, we study the problem of privacy protection in outsourcing frequent itemset mining. This problem has two challenges. One is on how to protect sensitive information, including the raw data and the frequent itemsets, with reasonable overhead and preserve the precise mining results. The other is how to protect against an attacker with related background knowledge such as item support information. To overcome these challenges, we propose k-support anonymity and develop a novel encryption approach that constructs a pseudo taxonomy tree to hide sensitive items. By leveraging the property that only the items at the leaf level of the taxonomy need to be appear at the transactions, the storage overhead is limited while the privacy protection is conformed. Second, note that data collected by sensors can consist of not only geographic attributes but also informative attributes. Since the spatial-alone clustering approaches consider only the geographic attributes to identify spatial clusters at data-dense regions, it is infeasible to obtain spatial clusters with informatively similar data points from such data by the spatial-alone clustering approaches. Therefore, we address the informative spatial data clustering (ISDC) problem in Chapter 3. One of the main challenges in this problem is that geographic and informative attributes represent different concepts and should not be tackled in the same way in clustering. To overcome this challenge, we proposed Algorithm BiAgree that introduces a graph structure, named NeiGraph, to integrate informative attributes and geographic attributes in vertices and edges, respectively. Afterward, Algorithm BiAgree is able to identify informatively similar regions regardless of the data density by partitioning NeiGraph into informative-consistent connected components. In addition, by maintaining NeiGraph, Algorithm BiAgree also provides the online computing capability to acquire the solutions with high quality and smaller computation time respectively. Finally, as the rapid growth in the number of services and applications leverage social network data, there is increasing concern about privacy issues in published social networks. Recently several studies have addressed the privacy issues on vertex/edge attributes, vertex identity, link disclosure, and so on. However, compared to the rich information inherent in graph data, the privacy issues in publications of social networks have not been fully solved. In Chapter 4, we address a new privacy issue, referred to as the community identification. The community identity of an individual is a kind of structural information that indicates the neighborhood or connections of the individual. The community identity could also represent the personal privacy information sensitive to the public, such as on-line political activity group, on-line disease support group information, or friend group in a social network. To protect such information, therefore, we propose a new privacy model, named k-structural diversity, and develop an Integer Programming formulation to find the optimal solutions to k-SDA. Moreover, we devise three scalable heuristics to solve the large instances of k-SDA with different perspectives.
Balasundaram, Balabhaskar. "Graph theoretic generalizations of clique: optimization and extensions." 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1539.
Full textLehončák, Michal. "Analýza odvozených sociálních sítí." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-448611.
Full textMěkota, Ondřej. "Predikce spojení v odvozených sociálních sítích." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-448563.
Full textZhao, Tao. "Identification of Online Users' Social Status via Mining User-Generated Data." Doctoral thesis, 2019. http://hdl.handle.net/21.11130/00-1735-0000-0003-C1B1-A.
Full textKetterl, Markus. "Scalable Multimedia Learning: From local eLectures to global Opencast." Doctoral thesis, 2014. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2014032712324.
Full text(11048391), Hao Sha. "SOLVING PREDICTION PROBLEMS FROM TEMPORAL EVENT DATA ON NETWORKS." Thesis, 2021.
Find full textMany complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of inferring the unknown network topology or links, based on the events, is also of great important. Examples along this line include: constructing influence networks among Twitter users from their tweets, soliciting new members to join an event based on their participation history, and recommending positions for job seekers according to their work experience. Following this direction, we study two types of problems - (1) recovering influence networks, and (2) predicting links between a node and a group of nodes, from event sequences.
Gordon, Jesse. "When data crimes are real crimes: voter surveillance and the Cambridge Analytica conflict." Thesis, 2019. http://hdl.handle.net/1828/11075.
Full textGraduate
Pitcher, Sandra. "The mass collaboration of digital information : an ethical examination of YouTube and intellectual property rights." Thesis, 2010. http://hdl.handle.net/10413/565.
Full textThesis (M.A.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.