Master of Science / Department of Computing and Information Sciences / Doina Caragea / The idea of socializing with other people of different backgrounds and cultures excites the web surfers. Today, there are hundreds of Social Networking sites on the web with millions of users connected with relationships such as "friend", "follow", "fan", forming a huge graph structure. The amount of data associated with the users in these Social Networking sites has resulted in opportunities for interesting data mining problems including friendship link and interest predictions, tag recommendations among others. In this work, we consider the friendship link prediction problem and study a topic modeling approach to this problem. Topic models are among the most effective approaches to latent topic analysis and mining of text data. In particular, Probabilistic Topic models are based upon the idea that documents can be seen as mixtures of topics and topics can be seen as mixtures of words. Latent Dirichlet Allocation (LDA) is one such probabilistic model which is generative in nature and is used for collections of discrete data such as text corpora. For our link prediction problem, users in the dataset are treated as "documents" and their interests as the document contents. The topic probabilities obtained by modeling users and interests using LDA provide an explicit representation for each user. User pairs are treated as examples and are represented using a feature vector constructed from the topic probabilities obtained with LDA. This vector will only capture information contained in the interests expressed by the users. Another important source of information that is relevant to the link prediction task is given by the graph structure of the social network. Our assumption is that a user "A" might be a friend of user "B" if a) users "A" and "B" have common or similar interests b) users "A" and "B" have some common friends. While capturing similarity between interests is taken care by the topic modeling technique, we use the graph structure to find common friends. In the past, the graph structure underlying the network has proven to be a trustworthy source of information for predicting friendship links. We present a comparison of predictions from feature sets constructed using topic probabilities and the link graph separately, with a feature set constructed using both topic probabilities and link graph.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / Recent advances in social network applications have resulted in millions of users joining such networks in the last few years. User data collected from social networks can be used for various data mining problems such as interest recommendations, friendship recommendations and many more. Social networks, in general, can be seen as a huge directed network graph representing users of the network (together with their information, e.g., user interests) and their interactions (also known as friendship links). Previous work [Hsu et al., 2007] on friendship link prediction has shown that graph features contain important predictive information. Furthermore, it has been shown that user interests can be used to improve link predictions, if they are organized into an explicitly or implicitly ontology [Haridas, 2009; Parimi, 2010]. However, the above mentioned previous studies have been performed using a small set of users in the social network LiveJournal. The goal of this work is to study the performance of the ontology based approach proposed in [Haridas, 2009], when number of users in the dataset is increased. More precisely, we study the performance of the approach in terms of performance for data sets consisting of 1000, 2000, 3000 and 4000 users. Our results show that the performance generally increases with the number of users. However, the problem becomes quickly intractable from a computation time point of view. As a part of our study, we also compare our results obtained using the ontology-based approach [Haridas, 2009] with results obtained with the LDA based approach in [Parimi, 2010], when such results are available.

Call number: LD2668 .T4 1985 S565 / Master of Science

Master of Science / Department of Computing and Information Sciences / William H. Hsu / This thesis discusses the background and methodologies necessary for constructing features in order to discover hidden links in relational data. Specifically, we consider the problems of predicting, classifying and annotating friends relations in friends networks, based upon features constructed from network structure and user profile data. I first document a data model for the blog service LiveJournal, and define a set of machine learning problems such as predicting existing links and estimating inter-pair distance. Next, I explain how the problem of classifying a user pair in a social networks, as directly connected or not, poses the problem of selecting and constructing relevant features. In order to construct these features, a genetic programming approach is used to construct multiple symbol trees with base features as their leaves; in this manner, the genetic program selects and constructs features that many not have been considered, but possess better predictive properties than the base features. In order to extract certain graph features from the relatively large social network, a new shortest path search algorithm is presented which computes and operates on a Euclidean embedding of the network. Finally, I present classification results and discuss the properties of the frequently constructed features in order to gain insight on hidden relations that exists in this domain.

Doctor of Philosophy / Department of Computing and Information Sciences / William H. Hsu / Association rule learning is a data mining technique that can capture relationships between pairs of entities in different domains. The goal of this research is to discover factors from data that can improve the precision, recall, and accuracy of association rules found using interestingness measures and frequent itemset mining. Such factors can be calibrated using validation data and applied to rank candidate rules in domain-dependent tasks such as link existence prediction. In addition, I use interestingness measures themselves as numerical features to improve link existence prediction. The focus of this dissertation is on developing and testing an analytical framework for association rule interestingness measures, to make them sensitive to the relative size of itemsets. I survey existing interestingness measures and then introduce adaptive parametric models for normalizing and optimizing these measures, based on the size of itemsets containing a candidate pair of co-occurring entities. The central thesis of this work is that in certain domains, the link strength between entities is related to the rarity of their shared memberships (i.e., the size of itemsets in which they co-occur), and that a data-driven approach can capture such properties by normalizing the quantitative measures used to rank associations. To test this hypothesis under different levels of variability in itemset size, I develop several test bed domains, each containing an association rule mining task and a link existence prediction task. The definitions of itemset membership and link existence in each domain depend on its local semantics. My primary goals are: to capture quantitative aspects of these local semantics in normalization factors for association rule interestingness measures; to represent these factors as quantitative features for link existence prediction, to apply them to significantly improve precision and recall in several real-world domains; and to build an experimental framework for measuring this improvement, using information theory and classification-based validation.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / William H. Hsu / An ontology can be seen as an explicit description of the concepts and relationships that exist in a domain. In this thesis, we address the problem of building an interests' ontology and using the same to construct features for predicting both potential friendship relations between users in the social network Live Journal, and users' interests. Previous work has shown that the accuracy of predicting friendship links in this network is very low if simply interests common to two users are used as features and no network graph features are considered. Thus, our goal is to organize users' interests into an ontology (specifically, a concept hierarchy) and to use the semantics captured by this ontology to improve the performance of learning algorithms at the task of predicting if two users can be friends. To achieve this goal, we have designed and implemented a hybrid clustering algorithm, which combines hierarchical agglomerative and divisive clustering paradigms, and automatically builds the interests' ontology. We have explored the use of this ontology to construct interest-based features and shown that the resulting features improve the performance of various classifiers for predicting friendships in the Live Journal social network. We have also shown that using the interests' ontology, one can address the problem of predicting the interests of Live Journal users, a task that in absence of the ontology is not feasible otherwise as there is an overwhelming number of interests.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper is a follow up to a paper presented at ITC 2002 entitled “Wireless Local Area Network for Data Telemetry from Fast Moving Nodes” by R. J. Bamberger, G. R. Barrett, R. A. Nichols, and J. L. Burbank of the Johns Hopkins University Applied Physics Laboratory, and M. H. Lauss of the Yuma Test Center at the U.S. Army Yuma Proving Ground (YPG). In that paper, network-centric data telemetry systems, specifically those based on commercial off- the-shelf (COTS) technologies such as the IEEE 802.11b Wireless Local Area Network (WLAN), were offered as an improvement over traditional frequency modulated (FM) data telemetry systems. The feasibility study of using WLANs for data telemetry considered both the radio frequency (RF) link over extended ranges and the effect due to Doppler shift. This paper describes an experiment designed to test those previous analyses.

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / In a quest to provide networked communication to test assets at all of the Major Range and Test Facility Bases (MRTFB), the integrated Network Enhanced Telemetry (iNET) Program was formed. A study was accomplished outlining five environments that encompass the work of these MRTFBs. The first of these environments to be advanced towards networked communication is the Aeronautical Environment. In order to develop these technologies, a test platform is proposed, realized, and tested. This airborne test platform will be used for concept and product testing and validation of the three portions of the Telemetry Network System (TmNS); the vehicle network, vNET, the radio frequency network (RF), rfNET, and the interface to the ground network, gNET. This paper will present the baseline system configuration, describe its operation, and detail RF link testing results.

ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / This paper presents the dynamic RF link estimation result for telemetry system of KSLV (Korea Space Launch Vehicle)-I. In particular, it utilizes the parameters of the instantaneous vehicle antenna gain pattern in three dimensions, the improvement by polarization diversity combiner at the ground receiver, and the free space propagation loss. The structural transformation and discontinuity of ground plane after the separation events of nose fairing, stage, and spacecraft, are also included in this analysis. As a consequence, the prediction of link variation has been performed in accordance with ARDP (Antenna Radiation Distribution Plot) and look angle trace of vehicle. In addition, the optimum position of onboard antennas has been investigated to provide better RF link margin in the nominal trajectory.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / As more satellite ground station systems use the Internet as a means of connectivity, the security of the ground stations and data transferred between stations becomes a growing concern. Possible solutions include software-level password authentication, link encryption, IP filtering, and several others. Many of these methods are being implemented in many different applications. SSH (Secure Shell) tunneling is one specific method that ensures a highly encrypted data link between computers on the Internet. It is used every day by individuals and organizations that want to ensure the security of the data they are transferring over the Internet. This paper describes the security requirements of a specific example of a ground station network, how SSH can be implemented into the existing system, software configuration, and operational testing of the revised ground network.