Dissertations / Theses on the topic 'Heterogeneous Developer Social Network'

With the popularity of social networks, users can communicate with each other in a more convenient way. However, the increasing amount of data poses new challenges for the analysis of the social activities of the users. In this article, we propose to visualize the heterogeneous information of user interactions in a social network in a three-dimensional way using the concept of solar systems. The target user represents the center of the solar system. We determine physical variables from interaction frequencies between the user and their friends to be visualized. This gives the user a better insight about their relationships on Facebook. To show the interactions between the user and their friends, we choose four variables related to the solar system: the size of each planet, its angular velocity, and the semi-major and the semi-minor axis of its elliptical orbit. Our system measures the interaction frequencies between a user and their friends based on a linear model. Its coefficients are derived from an online survey we performed. The experimental results indicate that the accuracy of our estimated interaction frequency is better than the accuracy for each individual interaction feature. The average accuracy improvement is 15.93%.

The Bass model has been used extensively and globally to forecast the first purchases of new products. It has been named by INFORMS as one of the top 10 most influential papers published in the 50-year history of Management Science. Most models for the diffusion of innovation are deeply rooted in the work of Bass (1969). His work provides a framework to model the underlying process of innovation adaption among first-time customers. Potential customers may be connected to one another in some sort of network. Prior research has shown that the structure of a network affects adoption patterns (Dover et al. 2012; Hill et al. 2006; Katona and Sarvary 2008; Katona et al. 2011; Newman et al. 2006; Shaikh et al. 2010; Van den Bulte and Joshi 2007). One approach to addressing this issue is to incorporate network information into the original Bass model. The focus of this study is to explore how to incorporate network information and other micro-level data into the Bass model. First, I prove that the Bass Model assumes all potential customers are linked to all other customers. Through simulations of individual adoptions and connections among individuals using a Random Network , I show that the estimate of q in the Bass Model is biased downward in the original Bass model. I find that biases in the Bass Model depend on the structure of the network. I relax the assumption of the fully connected network by proposing a Network-Based Bass model (NBB), which incorporates the network structure into the traditional Bass model. Using the proposed model (NBB), I am able to recover the true parameters. To test the generalizability and to enhance the applicability of my NBB model, I tested my NBB model on the various network types with sampled data from the population network. I showed that my NBB model is robust across different types of networks, and it is efficient in terms of sample size. With a small fraction of data from the population, it accurately recovered the true parameters. Therefore, the NBB model can be used when we do not have complete network information. The last essay is the first attempt to incorporate heterogeneous peer influence into the NBB model, based on individuals' preference structures. Besides the significant extension of the NBB (Bass) Model, incorporating high-quality data on individual behavior into the model leads to new findings on individuals' adoption behaviors, and thus expands our knowledge of the diffusion process.

The recent development of urban areas and of the new advanced services supported by digital technologies has generated big challenges for people and city administrators, like air pollution, high energy consumption, traffic congestion, management of public events. Moreover, understanding the perception of citizens about the provided services and other relevant topics can help devising targeted actions in the management. With the large diffusion of sensing technologies and user devices, the capability to generate data of public interest within the urban area has rapidly grown. For instance, different sensors networks deployed in the urban area allow collecting a variety of data useful to characterize several aspects of the urban environment. The huge amount of data produced by different types of devices and applications brings a rich knowledge about the urban context. Mining big urban data can provide decision makers with knowledge useful to tackle the aforementioned challenges for a smart and sustainable administration of urban spaces. However, the high volume and heterogeneity of data increase the complexity of the analysis. Moreover, different sources provide data with different spatial and temporal references. The extraction of significant information from such diverse kinds of data depends also on how they are integrated, hence alternative data representations and efficient processing technologies are required. The PhD research activity presented in this thesis was aimed at tackling these issues. Indeed, the thesis deals with the analysis of big heterogeneous data in smart city scenarios, by means of new data mining techniques and algorithms, to study the nature of urban related processes. The problem is addressed focusing on both infrastructural and algorithmic layers. In the first layer, the thesis proposes the enhancement of the current leading techniques for the storage and elaboration of Big Data. The integration with novel computing platforms is also considered to support parallelization of tasks, tackling the issue of automatic scaling of resources. At algorithmic layer, the research activity aimed at innovating current data mining algorithms, by adapting them to novel Big Data architectures and to Cloud computing environments. Such algorithms have been applied to various classes of urban data, in order to discover hidden but important information to support the optimization of the related processes. This research activity focused on the development of a distributed framework to automatically aggregate heterogeneous data at multiple temporal and spatial granularities and to apply different data mining techniques. Parallel computations are performed according to the MapReduce paradigm and exploiting in-memory computing to reach near-linear computational scalability. By exploring manifold data resolutions in a relatively short time, several additional patterns of data can be discovered, allowing to further enrich the description of urban processes. Such framework is suitably applied to different use cases, where many types of data are used to provide insightful descriptive and predictive analyses. In particular, the PhD activity addressed two main issues in the context of urban data mining: the evaluation of buildings energy efficiency from different energy-related data and the characterization of people's perception and interest about different topics from user-generated content on social networks. For each use case within the considered applications, a specific architectural solution was designed to obtain meaningful and actionable results and to optimize the computational performance and scalability of algorithms, which were extensively validated through experimental tests.

The Internet of Things paradigm establishes interaction and communication with a huge amount of actors. The concept is not a new-from-scratch one; actually, it combines a vast number of technologies and protocols and surely adaptations of pre-existing elements to offer new services and applications. One of the key technologies of the Internet of Things is the Radio Frequency Identification just abbreviated RFID. This technology proposes a set of solutions that allow tracking and tracing persons, animals and practically any item wirelessly. Considering the Internet of Things concept, multiple technologies need to be linked in order to provide interactions that lead to the implementation of services and applications. The challenge is that these technologies are not necessarily compatible and designed to work with other technologies. Within this context, the main objective of this thesis is to design a heterogeneous framework that will permit the interaction of diverse devices such as RFID, sensors and actuators in order to provide new applications and services. For this purpose in this work, our first contribution is the design and analysis of an integration architecture for heterogeneous devices. In the second contribution, we propose an evaluation model for RFID topologies and an optimization tool that assists in the RFID network planning process. Finally, in our last contribution, we implemented a simplified version of the framework by using embedded hardware and performance metrics are provided as well as the detailed configuration of the test platform

Le paradigme de l'Internet des choses établit l'interaction et la communication avec une énorme quantité d'acteurs. Le concept combine un grand nombre de technologies et de protocoles et des adaptations des éléments préexistants pour offrir de nouveaux services et applications. Une des technologies clés de l'Internet des objets est l'identification par radiofréquence abrégée en anglais RFID («Radio Frequency Identification»). Elle propose un ensemble de solutions qui permettent le suivi et la traçabilité des personnes, des animaux et pratiquement n'importe quel objet en utilisant des liaisons sans fil. En considérant le concept de l'Internet des choses, plusieurs technologies doivent être liées afin de fournir des interactions qui conduisent à la mise en œuvre de services et d'applications. Le défi est que ces technologies ne sont pas nécessairement compatibles et conçues pour fonctionner ensemble. Dans ce contexte, l'objectif principal de cette thèse est de concevoir un « framework » hétérogène qui permettra l'interaction de divers dispositifs tels que la RFID, des capteurs et des actionneurs afin de fournir de nouvelles applications et de services. À cet effet, notre première contribution est la conception et l'analyse d'une architecture d'intégration pour les dispositifs hétérogènes. Dans la seconde contribution, nous proposons un modèle d'évaluation de la topologie RFID et un outil d'optimisation pour la planification de réseaux de cette technologie. Enfin, nous avons implémenté une version simplifiée du framework en utilisant du matériel embarqué et indicateurs de performance sont fournis ainsi que la configuration détaillée de la plateforme de test<br>The Internet of Things paradigm establishes interaction and communication with a huge amount of actors. The concept is not a new-from-scratch one; actually, it combines a vast number of technologies and protocols and surely adaptations of pre-existing elements to offer new services and applications. One of the key technologies of the Internet of Things is the Radio Frequency Identification just abbreviated RFID. This technology proposes a set of solutions that allow tracking and tracing persons, animals and practically any item wirelessly. Considering the Internet of Things concept, multiple technologies need to be linked in order to provide interactions that lead to the implementation of services and applications. The challenge is that these technologies are not necessarily compatible and designed to work with other technologies. Within this context, the main objective of this thesis is to design a heterogeneous framework that will permit the interaction of diverse devices such as RFID, sensors and actuators in order to provide new applications and services. For this purpose in this work, our first contribution is the design and analysis of an integration architecture for heterogeneous devices. In the second contribution, we propose an evaluation model for RFID topologies and an optimization tool that assists in the RFID network planning process. Finally, in our last contribution, we implemented a simplified version of the framework by using embedded hardware and performance metrics are provided as well as the detailed configuration of the test platform

Over the last few decades, we have been witnessing a tremendous evolution in mobile computing, wireless networking and hand-held devices. In the future communication networks, users are anticipated to become even more mobile demanding for ubiquitous connectivity to different applications which will be preferably aware of their context. Admittedly, location information as part of their context is of paramount importance from both application and network perspectives. From application or user point of view, service provision can upgrade if adaptation to the user's context is enabled. From network point of view, functionalities such as routing, handoff management, resource allocation and others can also benefit if user's location can be tracked or even predicted. Within this context, we focus our attention on indoor localization and handoff prediction which are indispensable components towards the ultimate success of the envisioned pervasive communication era. While outdoor positioning systems have already proven their potential in a wide range of commercial applications, the path towards a successful indoor location system is recognized to be much more difficult, mainly due to the harsh indoor characteristics and requirement for higher accuracy. Similarly, handoff management in the future heterogeneous wireless networks is much more challenging than in traditional homogeneous networks. Handoff schemes must be seamless for meeting strict Quality of Service (QoS) requirements of the future applications and functional despite the diversity of operation features of the different technologies. In addition, handoff decisions should be flexible enough to accommodate user preferences from a wide range of criteria offered by all technologies. The main objective of this thesis is to devise accurate, time and power efficient location and handoff management systems in order to satisfy better context-aware and mobile applications. For indoor localization, the potential of Wireless Local Area Network (WLAN) and Radio Frequency Identification (RFID) technologies as standalone location sensing technologies are first studied by testing several algorithms and metrics in a real experimental testbed or by extensive simulations, while their shortcomings are also identified. Their integration in a common architecture is then proposed in order to combine their key benefits and overcome their limitations. The performance superiority of the synergetic system over the stand alone counterparts is validated via extensive analysis. Regarding the handoff management task, we pinpoint that context awareness can also enhance the network functionality. Consequently, two such schemes which utilize information obtained from localization systems are proposed. The first scheme relies on a RFID tag deployment, alike our RFID positioning architecture, and by following the WLAN scene analysis positioning concept, predicts the next network layer location, i.e. the next point of attachment to the network. The second scheme relies on an integrated RFID and Wireless Sensor/Actuator Network (WSAN) deployment for tracking the users' physical location and subsequently for predicting next their handoff point at both link and network layers. Being independent of the underlying principle wireless access technology, both schemes can be easily implemented in heterogeneous networks. Performance evaluation results demonstrate the advantages of the proposed schemes over the standard protocols regarding prediction accuracy, time latency and energy savings

Au cours des dernières décennies, nous avons été témoins d'une évolution considérable dans l'informatique mobile, réseau sans fil et des appareils portatifs. Dans les réseaux de communication à venir, les utilisateurs devraient être encore plus mobiles exigeant une connectivité omniprésente à différentes applications qui seront de préférence au courant de leur contexte. Certes, les informations de localisation dans le cadre de leur contexte est d'une importance primordiale à la fois la demande et les perspectives du réseau. Depuis l'application ou de point de vue utilisateur, la fourniture de services peut mettre à jour si l'adaptation au contexte de l'utilisateur est activée. Du point de vue du réseau, des fonctionnalités telles que le routage, la gestion de transfert, l'allocation des ressources et d'autres peuvent également bénéficier si l'emplacement de l'utilisateur peuvent être suivis ou même prédit. Dans ce contexte, nous nous concentrons notre attention sur la localisation à l'intérieur et de la prévision transfert qui sont des composants indispensables à la réussite ultime de l'ère de la communication omniprésente envisagé. Alors que les systèmes de positionnement en plein air ont déjà prouvé leur potentiel dans un large éventail d'applications commerciales, le chemin vers un système de localisation à l'intérieur de succès est reconnu pour être beaucoup plus difficile, principalement en raison des caractéristiques difficiles à l'intérieur et l'exigence d'une plus grande précision. De même, la gestion de transfert dans le futur des réseaux hétérogènes sans fil est beaucoup plus difficile que dans les réseaux traditionnels homogènes. Régimes de procédure de transfert doit être sans faille pour la réunion strictes de qualité de service (QoS) des applications futures et fonctionnel malgré la diversité des caractéristiques de fonctionnement des différentes technologies. En outre, les décisions transfert devraient être suffisamment souples pour tenir compte des préférences utilisateur d'un large éventail de critères proposés par toutes les technologies. L'objectif principal de cette thèse est de mettre au point précis, l'heure et l'emplacement de puissance et de systèmes efficaces de gestion de transfert afin de mieux satisfaire applications sensibles au contexte et mobiles. Pour obtenir une localisation à l'intérieur, le potentiel de réseau local sans fil (WLAN) et Radio Frequency Identification (RFID) que l'emplacement autonome technologies de détection sont d'abord étudiés par des essais plusieurs algorithmes et paramètres dans un banc d'essai expérimental réel ou par de nombreuses simulations, alors que leurs lacunes sont également été identifiés. Leur intégration dans une architecture commune est alors proposée afin de combiner leurs principaux avantages et surmonter leurs limitations. La supériorité des performances du système de synergie sur le stand alone homologues est validée par une analyse approfondie. En ce qui concerne la tâche de gestion transfert, nous repérer que la sensibilité au contexte peut aussi améliorer la fonctionnalité du réseau. En conséquence, deux de tels systèmes qui utilisent l'information obtenue à partir des systèmes de localisation sont proposées. Le premier schéma repose sur un déploiement tag RFID, comme notre architecture de positionnement RFID, et en suivant la scène WLAN analyse du concept de positionnement, prédit l'emplacement réseau de la prochaine couche, c'est à dire le prochain point de fixation sur le réseau. Le second régime repose sur une approche intégrée RFID et sans fil de capteur / actionneur Network (WSAN) de déploiement pour la localisation des utilisateurs physiques et par la suite pour prédire la prochaine leur point de transfert à deux couches de liaison et le réseau. Etre indépendant de la technologie d'accès sans fil principe sous-jacent, les deux régimes peuvent être facilement mises en œuvre dans des réseaux hétérogènes [...]<br>Over the last few decades, we have been witnessing a tremendous evolution in mobile computing, wireless networking and hand-held devices. In the future communication networks, users are anticipated to become even more mobile demanding for ubiquitous connectivity to different applications which will be preferably aware of their context. Admittedly, location information as part of their context is of paramount importance from both application and network perspectives. From application or user point of view, service provision can upgrade if adaptation to the user's context is enabled. From network point of view, functionalities such as routing, handoff management, resource allocation and others can also benefit if user's location can be tracked or even predicted. Within this context, we focus our attention on indoor localization and handoff prediction which are indispensable components towards the ultimate success of the envisioned pervasive communication era. While outdoor positioning systems have already proven their potential in a wide range of commercial applications, the path towards a successful indoor location system is recognized to be much more difficult, mainly due to the harsh indoor characteristics and requirement for higher accuracy. Similarly, handoff management in the future heterogeneous wireless networks is much more challenging than in traditional homogeneous networks. Handoff schemes must be seamless for meeting strict Quality of Service (QoS) requirements of the future applications and functional despite the diversity of operation features of the different technologies. In addition, handoff decisions should be flexible enough to accommodate user preferences from a wide range of criteria offered by all technologies. The main objective of this thesis is to devise accurate, time and power efficient location and handoff management systems in order to satisfy better context-aware and mobile applications. For indoor localization, the potential of Wireless Local Area Network (WLAN) and Radio Frequency Identification (RFID) technologies as standalone location sensing technologies are first studied by testing several algorithms and metrics in a real experimental testbed or by extensive simulations, while their shortcomings are also identified. Their integration in a common architecture is then proposed in order to combine their key benefits and overcome their limitations. The performance superiority of the synergetic system over the stand alone counterparts is validated via extensive analysis. Regarding the handoff management task, we pinpoint that context awareness can also enhance the network functionality. Consequently, two such schemes which utilize information obtained from localization systems are proposed. The first scheme relies on a RFID tag deployment, alike our RFID positioning architecture, and by following the WLAN scene analysis positioning concept, predicts the next network layer location, i.e. the next point of attachment to the network. The second scheme relies on an integrated RFID and Wireless Sensor/Actuator Network (WSAN) deployment for tracking the users' physical location and subsequently for predicting next their handoff point at both link and network layers. Being independent of the underlying principle wireless access technology, both schemes can be easily implemented in heterogeneous networks. Performance evaluation results demonstrate the advantages of the proposed schemes over the standard protocols regarding prediction accuracy, time latency and energy savings

碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>100<br>Social network analysis has been a hot topic in the last few years. Due to the rise of social network sites such as Twitter, Plurk, and Facebook, large amount of data is available for the research world. However, not all social network sites make their full customer database available to general public. Often times, engineers need to write crawlers to crawl websites just to obtain parts of the social network. Data Sampling has been widely used to extract a subset of social network to represent the larger network. Various network properties have been proposed to measure the similarity between the sampled sub-networks with the original network. However, some of the properties only work for homogenous networks, in which nodes and edges are treated the same. In this thesis, we propose a novel network property, the Relational Profile to model the transitional probability between node and link types in a heterogeneous social network, networks which nodes and edges have different types. We propose a novel sampling by exploration method with the goal to sample a sub-network whose Relational Profile is as close to the Relational Profile of the original network as possible. The experiment result shows that our sampling method produces a more representative sub-network with less sampled nodes and edges. Then we try to solve a real world problem, node type prediction, using Machine Learning method with sampled sub-network as training data. Experiment shows that using Relational Profile as features works better than other features, such as in and out degree, as Relational Profile is more resistant to neighbors of testing nodes missing. Also, with the same amount of nodes sampled, sub-networks created by our sampling method can predict node types with higher accuracy than other baseline methods.

碩士<br>國立臺灣大學<br>資訊工程學研究所<br>99<br>In recent years, social network analysis becomes a topic that raises wide interest in. There are many existing works on analyzing important network properties on homogeneous social network, such as clustering coefficient, centrality and betweenness. But such works on heterogeneous social network are limited. Additionally, many of these analysis need to perform high complexity computation on the network, which is not practical on the large scale social networks provided by modern online social network sites. To gain better understanding to such large-scale network in a reasonable time, graph sampling becomes an important method to reduce the scale of network and preserve important properties in the original network in the same time. In this paper, we proposed relational profile, which is a network property contains the conditional probabilities between various types in the heterogeneous social network to capture the dependence between types in the heterogeneous social network. Then, we proposed a novel sampling problem, relational profile preserving sampling. We also proposed sampling methods, Difference Maximization Sampling and Difference Proportion Sampling, which designed to solve the sampling problem. We evaluated our sampling methods on three real-world networks and show that our algorithm outperforms other common sampling algorithms when the sample size is small. Finally, We discussed the effect of relational profile and sample size on the performance of different sample methods.

碩士<br>國立高雄第一科技大學<br>資訊管理研究所<br>102<br>Recently, the No-SQL concept has been widely adopted in the database processing for social networking. However, prior studies usually focus on the issues concerning graph partitioning for distributed graph databases, which main concern was conducted on the “distributed processing” task. There are rarely papers considering about how to perform integration on heterogeneous graph databases, to combine two different social networks for better synergy. Therefore, in this paper, we intent to address the integration problem of heterogeneous graph databases. We separately imported both Facebook and Google+ data into Neo4j, a graph database, and utilized the model of Probabilistic Similarity Logic (PSL) to infer the similarity between attributes, which will be used to construct the graph invariants. By creating a Control Management mechanism for controlling the updating and querying tasks for both databases through the obtained graph invariants, we can generate the metadata index for both databases, which can be used to integrate both social networks by acquiring the similar data. By integrating social networks, the obtained result will be fruitful for analyzing user behavior between different social networks. For business marketing analysis, it is helpful to develop more profitable sales model, and users can cross different social platforms to enrich better social relationships management.

碩士<br>國立臺灣大學<br>資訊工程學研究所<br>102<br>This paper proposes to study a novel problem, discovering a Smallest Unique Subgraph (SUS) for any node of interest specified by user in a heterogeneous social network. The rationale of the SUS problem lies in how a person is different from any others in a social network, and how to represent the identity of a person using her surrounding relational structure in a social network. To deal with the proposed SUS problem, we develop an Ego-Graph Heuristic (EGH) method to efficiently solve the SUS problem in an approximated manner. EGH intelligently examine whether one graph is not isomorphic to the other, instead of using the conventional subgraph isomorphism test. We also prove SUS is a NP-complete problem through doing a reduction from Minimum Vertex Cover (MVC) in a homogeneous tree structure. Experimental results conducted on a real-world movie heterogeneous social network data show both the promising efficiency and compactness of our method.

Research on social networks and understanding the interactions of the users can be modeled as a task of graph mining, such as predicting nodes and edges in networks.Dealing with such unstructured data in large social networks has been a challenge for researchers in several years. Neural Networks have recently proven very successful in performing predictions on number of speech, image, and text data and have become the de facto method when dealing with such data in a large volume. Graph NeuralNetworks, however, have only recently become mature enough to be used in real large-scale graph prediction tasks, and require proper structure and data modeling to be viable and successful. In this research, we provide a new modeling of the social network which captures the attributes of the nodes from various dimensions. We also introduce the Neural Network architecture that is required for optimally utilizing the new data structure. Finally, in order to provide a hot-start for our model, we initialize the weights of the neural network using a pre-trained graph embedding method. We have also developed a new graph embedding algorithm. We will first explain how previous graph embedding methods are not optimal for all types of graphs, and then provide a solution on how to combat those limitations and come up with a new graph embedding method.

Indiana University-Purdue University Indianapolis (IUPUI)<br>Research on social networks and understanding the interactions of the users can be modeled as a task of graph mining, such as predicting nodes and edges in networks. Dealing with such unstructured data in large social networks has been a challenge for researchers in several years. Neural Networks have recently proven very successful in performing predictions on number of speech, image, and text data and have become the de facto method when dealing with such data in a large volume. Graph NeuralNetworks, however, have only recently become mature enough to be used in real large-scale graph prediction tasks, and require proper structure and data modeling to be viable and successful. In this research, we provide a new modeling of the social network which captures the attributes of the nodes from various dimensions. We also introduce the Neural Network architecture that is required for optimally utilizing the new data structure. Finally, in order to provide a hot-start for our model, we initialize the weights of the neural network using a pre-trained graph embedding method. We have also developed a new graph embedding algorithm. We will first explain how previous graph embedding methods are not optimal for all types of graphs, and then provide a solution on how to combat those limitations and come up with a new graph embedding method.

Social networks play an important role in disseminating a piece of information in a population. Companies advertising a newly launched product, movie promotion, political campaigns, social awareness campaigns by governments, charity campaigns by NGOs and crowd funding campaigns by entrepreneurs are a few examples where an entity is interested in disseminating a piece of information in a target population, possibly under resource constraints. In this thesis we model information diffusion in a population using various epidemic models and study optimal campaigning strategies to maximize the reach of information. In the different problems considered in this thesis, information epidemics are modeled as the Susceptible-Infected, Susceptible-Infected-Susceptible, Susceptible-Infected-Recovered and Maki Thompson epidemic processes; however, we modify the models to incorporate the intervention made by the campaigner to enhance information propagation. Direct recruitment of individuals as spreaders and providing word-of-mouth incentives to the spreaders are considered as two intervention strategies (controls) to enhance the speed of information propagation. These controls can be implemented by placing advertisements in the mass media, announcing referral/cash back rewards for introducing friends to a product or service being advertised etc. In the different problems considered in this thesis, social contacts are modeled with varying levels of complexity---population is homogeneously mixed or follows heterogeneous mixing. The solutions to the problems which consider homogeneous mixing of individuals identify the most important periods in the campaign duration which should be allocated more resources to maximize the reach of the message, depending on the system parameters of the epidemic model (e.g., epidemics with high and low virulence). When a heterogeneous model is considered, apart from this, the solution identifies the important classes of individuals which should be allocated more resources depending upon the network considered (e.g. Erdos-Renyi, scale-free) and model parameters. These classes may be carved out based on various centrality measures in the network. If multiple strategies are available for campaigning, the solution also identifies the relative importance of the strategies depending on the network type. We study variants of the optimal campaigning problem where we optimize different objective functions. For some of the formulated problems, we discuss the existence and uniqueness of the solution. Sometimes our formulations call for novel techniques to prove the existence of a solution.

Social networks play an important role in disseminating a piece of information in a population. Companies advertising a newly launched product, movie promotion, political campaigns, social awareness campaigns by governments, charity campaigns by NGOs and crowd funding campaigns by entrepreneurs are a few examples where an entity is interested in disseminating a piece of information in a target population, possibly under resource constraints. In this thesis we model information diffusion in a population using various epidemic models and study optimal campaigning strategies to maximize the reach of information. In the different problems considered in this thesis, information epidemics are modeled as the Susceptible-Infected, Susceptible-Infected-Susceptible, Susceptible-Infected-Recovered and Maki Thompson epidemic processes; however, we modify the models to incorporate the intervention made by the campaigner to enhance information propagation. Direct recruitment of individuals as spreaders and providing word-of-mouth incentives to the spreaders are considered as two intervention strategies (controls) to enhance the speed of information propagation. These controls can be implemented by placing advertisements in the mass media, announcing referral/cash back rewards for introducing friends to a product or service being advertised etc. In the different problems considered in this thesis, social contacts are modeled with varying levels of complexity---population is homogeneously mixed or follows heterogeneous mixing. The solutions to the problems which consider homogeneous mixing of individuals identify the most important periods in the campaign duration which should be allocated more resources to maximize the reach of the message, depending on the system parameters of the epidemic model (e.g., epidemics with high and low virulence). When a heterogeneous model is considered, apart from this, the solution identifies the important classes of individuals which should be allocated more resources depending upon the network considered (e.g. Erdos-Renyi, scale-free) and model parameters. These classes may be carved out based on various centrality measures in the network. If multiple strategies are available for campaigning, the solution also identifies the relative importance of the strategies depending on the network type. We study variants of the optimal campaigning problem where we optimize different objective functions. For some of the formulated problems, we discuss the existence and uniqueness of the solution. Sometimes our formulations call for novel techniques to prove the existence of a solution.