Η ολοένα και αυξανόμενη χρήση των ηλεκτρονικών υπολογιστών και του Διαδικτύου σε διάφορες εκφάνσεις της καθημερινότητας του ανθρώπου, έχει επιφέρει μια επανάσταση στο χώρο της τεχνολογίας, η οποία εξακολουθεί να υφίσταται στις μέρες μας. Η έλευση και η εδραίωση του Web 2.0 και των εργαλείων που το απαρτίζουν, σε συνδυασμό με την έμφυτη τάση του ανθρώπου για επικοινωνία, οδήγησαν με τη σειρά τους στην εμφάνιση των λεγόμενων Υπηρεσιών Κοινωνικής Δικτύωσης. Οι υπηρεσίες αυτές προσφέρουν δυνατότητες επικοινωνίας, ψυχαγωγίας αλλά και διαφήμισης και αποτελούν, πλέον, αναπόσπαστο κομμάτι της καθημερινότητας πολλών ανθρώπων παγκοσμίως. H αυξανόμενη χρήση των υπηρεσιών αυτών, σε συνδυασμό με την εμφάνιση και καθιέρωση των λεγόμενων έξυπνων κινητών συσκευών που είναι εξοπλισμένα με GPS και μπορούν να εντοπίζουν την τρέχουσα θέση του εκάστοτε χρήστη, οδήγησαν στον εμπλουτισμό των υφιστάμενων υπηρεσιών κοινωνικής δικτύωσης με χαρακτηριστικά τοποθεσίας, αλλά και στην ανάπτυξη μιας νέας κατηγορίας δικτύων, των λεγόμενων Υπηρεσιών Κοινωνικής Δικτύωσης που βασίζονται στην τοποθεσία. Οι υπηρεσίες αυτές διαθέτουν όλα τα γνωρίσματα των παραδοσιακών κοινωνικών δικτύων, ωστόσο το κύριο χαρακτηριστικό τους είναι ο εντοπισμός και ο διαμοιρασμός της γεωγραφικής θέσης του χρήστη. Σήμερα, οι υπηρεσίες κοινωνικής δικτύωσης που βασίζονται στην τοποθεσία είναι εξαιρετικά δημοφιλείς με εκατομμύρια χρήστες παγκοσμίως. Αυτή η αποδοχή και η εκτεταμένη χρήση τους έχουν ως αποτέλεσμα έναν εξαιρετικά μεγάλο όγκο δεδομένων, ο οποίος είναι διαθέσιμος μέσω των Διεπαφών Προγραμματισμού Εφαρμογών που αυτές διαθέτουν και έχει κεντρίσει το ενδιαφέρον των ερευνητών για μελέτη. Στα πλαίσια της παρούσας μεταπτυχιακής διπλωματικής εργασίας, θα μελετηθούν οι υπηρεσίες κοινωνικής δικτύωσης, οι υπηρεσίες κοινωνικής δικτύωσης που βασίζονται στην τοποθεσία, τα χαρακτηριστικά που αυτές διαθέτουν, καθώς και ορισμένα παραδείγματα τέτοιων υπηρεσιών. Επιπλέον, θα παρουσιαστεί η υλοποίηση μιας εφαρμογής, η οποία συλλέγει δεδομένα για 10 ευρωπαϊκά αεροδρόμια από τα API του Foursquare και του Facebook, αλλά και δεδομένα για διάφορα σημεία του Foursquare σε σχέση με δεδομένα καιρού από το API της υπηρεσίας OpenWeatherMap, η δημιουργία ενός διαχειριστικού περιβάλλοντος για το σκοπό αυτό, καθώς και τα αποτελέσματα από τη στατιστική ανάλυση των μετρήσεων. Στόχος μας είναι να διερευνήσουμε εάν τέτοιου είδους δεδομένα είναι αντιπροσωπευτικά των αντίστοιχων πραγματικών δεδομένων ή σχετίζονται με δεδομένα από άλλες διαδικτυακές υπηρεσίες. Αυτό γίνεται μελετώντας τη συσχέτιση των πληροφοριών που αντλούνται από το Foursquare με τα αντίστοιχα δεδομένα του πραγματικού κόσμου, τη συσχέτιση των δεδομένων που παράγονται από το Foursquare και το Facebook, αλλά και των δεδομένων του Foursquare σε σχέση με δεδομένα καιρού από την υπηρεσία OpenWeatherMap. / The more and more growing use of computers and the Internet in different situations of man’s everyday life, has brought about a technological revolution which continues happening in our days. The advent and the consolidation of Web 2.0 and the tools which form it, in combination with the man’s innate trend for communication, lead by their turn to the development of Social Network(ing) Services (SNS). These services give the chance to people for communication, entertainment, as well as advertising and they are an integral part in people’s everyday lives worldwide. The growing use of these services, in combination with the appearance and the establishment of the so called smart mobile devices which are equipped with GPS and can detect the exact location of every user, lead to the enrichment of the undergoing Social Network Services with location characteristics, but also to the development of a new category of networks, the Location-Based Social Networks (LBSN). These services have all the characteristics of the traditional social networks, however their main feature is the localization and the distribution of a user’s location. Today, the Location-Based Social Networks are extremely popular to millions of users all around the world. This acceptance and their extending use have as a result an extremely volume of data which is available through their Application Programming Interfaces (APIs) and it has roused the interest of researchers for study. Within the framework of the present postgraduate thesis, the Social Network Services, the Location-Based Social Networks, their features and some examples of these services will be studied. Moreover, the materialization of an application which collects data for 10 european airports from the APIs of Foursquare and Facebook, but also data for different venues of Foursquare in connection with weather data from the API of OpenWeatherMap, as well as the creation of an administrative environment for this purpose and the results of the statistical analysis of our measurements will be presented. Our goal is to investigate if such data are representative of real world data or if such data are related to data from other online services. This is done by studying the correlation of data which are derived by Foursquare with the equivalent data of the real world and the correlation of data which are produced by Foursquare and Facebook, as well as the correlation of data which are produced by Foursquare with weather data from OpenWeatherMap.

Grâce aux technologies web et mobiles, le partage de données entre utilisateurs a considérablement augmenté au cours des dernières années. Par exemple, les utilisateurs peuvent facilement enregistrer leurs trajectoires durant leurs déplacements quotidiens avec l'utilisation de récepteurs GPS et les mettre en relation avec les trajectoires d'autres utilisateurs. L'analyse des trajectoires des utilisateurs au fil du temps peut révéler des habitudes et préférences. Cette information peut être utilisée pour recommander des contenus à des utilisateurs individuels ou à des groupes d'utilisateurs avec des trajectoires ou préférences similaires. En revanche, l'enregistrement de points GPS génère de grandes quantités de données. Par conséquent, les algorithmes de clustering sont nécessaires pour analyser efficacement ces données. Dans cette thèse, nous nous concentrons sur l'étude des différentes solutions pour analyser les trajectoires, extraire les préférences et identifier les intérêts similaires entre les utilisateurs. Nous proposons un algorithme de clustering de trajectoires GPS. En outre, nous proposons un algorithme de corrélation basée sur les trajectoires des points proches entre deux ou plusieurs utilisateurs. Les résultats finaux ouvrent des perspectives intéressantes pour explorer les applications des réseaux sociaux basés sur la localisation. / Sharing of user data has substantially increased over the past few years facilitated by sophisticated Web and mobile applications, including social networks. For instance, users can easily register their trajectories over time based on their daily trips captured with GPS receivers as well as share and relate them with trajectories of other users. Analyzing user trajectories over time can reveal habits and preferences. This information can be used to recommend content to single users or to group users together based on similar trajectories and/or preferences. Recording GPS tracks generates very large amounts of data. Therefore clustering algorithms are required to efficiently analyze such data. In this thesis, we focus on investigating ways of efficiently analyzing user trajectories, extracting user preferences from them and identifying similar interests between users. We demonstrate an algorithm for clustering user GPS trajectories. In addition, we propose an algorithm to correlate trajectories based on near points between two or more users. The final results provided interesting avenues for exploring Location-based Social Network (LBSN) applications.

Online social networks (OSNs) are Web platforms providing different services to facilitate social interaction among their users. A particular kind of OSNs is the location-based social network (LBSN), which adds services based on location. One of the most important challenges in LBSNs is the link prediction problem. Link prediction problem aims to estimate the likelihood of the existence of future friendships among user pairs. Most of the existing studies in link prediction focus on the use of a single information source to perform predictions, i.e. only social information (e.g. social neighborhood) or only location information (e.g. common visited places). However, some researches have shown that the combination of different information sources can lead to more accurate predictions. In this sense, in this thesis we propose different link prediction methods based on the use of different information sources naturally existing in these networks. Thus, we propose seven new link prediction methods using the information related to user membership in social overlapping groups: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA) and group naïve Bayes of Resource Allocation (GNB-RA). Due to that social groups exist naturally in networks, our proposals can be used in any type of OSN.We also propose new eight link prediction methods combining location and social information: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP) and Nearby Distance Allocation (NDA). These eight methods are exclusively for work in LBSNs. Obtained results indicate that our proposals are as competitive as state-of-the-art methods, or better than they in certain scenarios. Moreover, since our proposals tend to be computationally more efficient, they are more suitable for real-world applications. / Redes sociais online (OSNs) são plataformas Web que oferecem serviços para promoção da interação social entre usuários. OSNs que adicionam serviços relacionados à geolocalização são chamadas redes sociais baseadas em localização (LBSNs). Um dos maiores desafios na análise de LBSNs é a predição de links. A predição de links refere-se ao problema de estimar a probabilidade de conexão futura entre pares de usuários que não se conhecem. Grande parte das pesquisas que focam nesse problema exploram o uso, de maneira isolada, de informações sociais (e.g. amigos em comum) ou de localização (e.g. locais comuns visitados). Porém, algumas pesquisas mostraram que a combinação de diferentes fontes de informação pode influenciar o incremento da acurácia da predição. Motivado por essa lacuna, neste trabalho foram desenvolvidos diferentes métodos para predição de links combinando diferentes fontes de informação. Assim, propomos sete métodos que usam a informação relacionada à participação simultânea de usuários en múltiples grupos sociais: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA), e group naïve Bayes of Resource Allocation (GNB-RA). Devido ao fato que a presença de grupos sociais não está restrita a alguns tipo de redes, essas propostas podem ser usadas nas diversas OSNs existentes, incluindo LBSNs. Também, propomos oito métodos que combinam o uso de informações sociais e de localização: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP), e Nearby Distance Allocation (NDA). Tais propostas são para uso exclusivo em LBSNs. Os resultados obtidos indicam que nossas propostas são tão competitivas quanto métodos do estado da arte, podendo até superá-los em determinados cenários. Ainda mais, devido a que na maioria dos casos nossas propostas são computacionalmente mais eficientes, seu uso resulta mais adequado em aplicações do mundo real.

La dynamique humaine est un sujet essentiel de l'informatique centrée sur l’homme. Elle se concentre sur la compréhension des régularités sous-jacentes, des relations, et des changements dans les comportements humains. En analysant la dynamique humaine, nous pouvons comprendre non seulement des comportements individuels, tels que la présence d’une personne à un endroit précis, mais aussi des comportements collectifs, comme les mouvements sociaux. L’exploration de la dynamique humaine permet ainsi diverses applications, entre autres celles des services géo-dépendants personnalisés dans des scénarios de ville intelligente. Avec l'omniprésence des smartphones équipés de GPS, les réseaux sociaux de géolocalisation ont acquis une popularité croissante au cours des dernières années, ce qui rend les données de comportements des utilisateurs disponibles à grande échelle. Sur les dits réseaux sociaux de géolocalisation, les utilisateurs peuvent partager leurs activités en temps réel avec par l'enregistrement de leur présence à des points d'intérêt (POIs), tels qu’un restaurant. Ces données d'activité contiennent des informations massives sur la dynamique humaine. Dans cette thèse, nous explorons la dynamique humaine basée sur les données massives des réseaux sociaux de géolocalisation. Concrètement, du point de vue individuel, nous étudions la préférence de l'utilisateur quant aux POIs avec des granularités différentes et ses applications, ainsi que la régularité spatio-temporelle des activités des utilisateurs. Du point de vue collectif, nous explorons la forme d'activité collective avec les granularités de pays et ville, ainsi qu’en corrélation avec les cultures globales / Human dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human cultures