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ItemPoint process modeling and optimization of social networks(Georgia Institute of Technology, 2018-04-05) Farajtabar, MehrdadOnline social media such as Facebook and Twitter and communities such as Wikipedia and Stackoverflow turn to become an inseparable part of today's lifestyle. Users usually participate via a variety of ways like sharing text and photos, asking questions, finding friends, and favoring contents. Theses activities produce sequences of events data whose complex temporal dynamics need to be studied and is of many practical, economic, and societal interest. We propose a novel framework based on multivariate temporal point processes that is used for modeling, optimization, and inference of processes taken place over networks. In the modeling part, we propose a temporal point process model for joint dynamics of information propagation and structure evolution in networks. These two highly intertwined stochastic processes have been predominantly studied separately, ignoring their co-evolutionary dynamics. Our model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. In the optimization part, we establish the fundamentals of intervention and control in networks by combining the rich area of temporal point processes and the well-developed framework of Markov decision processes. We use point processes to capture both endogenous and exogenous events in social networks and formulate the problem as a Markov decision problem. Our methodology helps finding the optimal policy that balances the high present reward and large penalty on low future outcome in the presence of extensive uncertainties. In the inference part, we propose an intensity-free approach for point processes modeling that transforms the nuisance process to the target one. Furthermore, we train our deep neural network model using a likelihood-free approach leveraging Wasserstein distance between point processes.
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ItemModeling, learning, and inference of high-dimensional asynchronous event data(Georgia Institute of Technology, 2016-05-05) Du, NanThe increasing availability of temporal-spatial events produced from natural and social systems provides new opportunities and challenges for effective modeling the latent dynamics which inherently govern these seemly ``random'' data. In our work, we propose a unified probabilistic framework based on multivariate point processes to better predict `who will do what by when and where?' in the future. This framework comprises a systematic paradigm for modeling, learning, and making inference of large-scale asynchronous high-dimensional event data. With this common framework, we contribute in the following three aspects. Accurate Modeling: we first propose non-parametric and topic-modulated multivariate terminating point processes to capture continuous-time heterogeneous information diffusions. We then develop the low-rank Hawkes process to describe the recurrent temporal interactions among different types of entities. We also build a link between the recurrent neural network and the temporal point process to learn a general representation of the influence from the past event history. Finally, we establish a previously unexplored connection between Bayesian Nonparametrics and temporal point processes to jointly model the temporal data and other type of additional information. Efficient Learning: we develop a robust structure learning algorithm via group lasso, which is able to efficiently uncover sparse heterogeneous interdependent relations specified via vectorized parameters among the dimensions. We also propose an efficient nonnegative matrix rank minimization algorithm, which elegantly inherits the advantages from both the proximal methods and the conditional gradient methods to solve the matrix rank minimization problem under different constraints. Finally, in the data streaming setting, we develop a Bayesian inference algorithm for inferring latent variables and updating the respective model parameters based on both temporal and textual information, which achieves almost constant processing time per data sample. Scalable Inference: another important aspect of our research is to make future predictions by exploiting the learned models. Specifically, based on the terminating processes, we develop the first scalable influence estimation algorithm in continuous-time diffusion networks with provable performance guarantees. Based on the low-rank Hawkes processes, we develop the first time-sensitive recommendation algorithm, which not only can recommend the most relevant item specific to a given moment, but also can predict the next returning time for a user to a designated service. Finally, based on the recurrent point processes, we have derived an analytic solution to shape the overall network activities of users. We show that our method can provide fine-grained control over user activities in a time-sensitive fashion.
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ItemInfluence modeling in behavioral data(Georgia Institute of Technology, 2015-05-15) Li, LiangdaUnderstanding influence in behavioral data has become increasingly important in analyzing the cause and effect of human behaviors under various scenarios. Influence modeling enables us to learn not only how human behaviors drive the diffusion of memes spread in different kinds of networks, but also the chain reactions evolve in the sequential behaviors of people. In this thesis, I propose to investigate into appropriate probabilistic models for efficiently and effectively modeling influence, and the applications and extensions of the proposed models to analyze behavioral data in computational sustainability and information search. One fundamental problem in influence modeling is the learning of the degree of influence between individuals, which we called social infectivity. In the first part of this work, we study how to efficient and effective learn social infectivity in diffusion phenomenon in social networks and other applications. We replace the pairwise infectivity in the multidimensional Hawkes processes with linear combinations of those time-varying features, and optimize the associated coefficients with lasso regularization on coefficients. In the second part of this work, we investigate the modeling of influence between marked events in the application of energy consumption, which tracks the diffusion of mixed daily routines of household members. Specifically, we leverage temporal and energy consumption information recorded by smart meters in households for influence modeling, through a novel probabilistic model that combines marked point processes with topic models. The learned influence is supposed to reveal the sequential appliance usage pattern of household members, and thereby helps address the problem of energy disaggregation. In the third part of this work, we investigate a complex influence modeling scenario which requires simultaneous learning of both infectivity and influence existence. Specifically, we study the modeling of influence in search behaviors, where the influence tracks the diffusion of mixed search intents of search engine users in information search. We leverage temporal and textual information in query logs for influence modeling, through a novel probabilistic model that combines point processes with topic models. The learned influence is supposed to link queries that serve for the same formation need, and thereby helps address the problem of search task identification. The modeling of influence with the Markov property also help us to understand the chain reaction in the interaction of search engine users with query auto-completion (QAC) engine within each query session. The fourth part of this work studies how a user's present interaction with a QAC engine influences his/her interaction in the next step. We propose a novel probabilistic model based on Markov processes, which leverage such influence in the prediction of users' click choices of suggested queries of QAC engines, and accordingly improve the suggestions to better satisfy users' search intents. In the fifth part of this work, we study the mutual influence between users' behaviors on query auto-completion (QAC) logs and normal click logs across different query sessions. We propose a probabilistic model to explore the correlation between user' behavior patterns on QAC and click logs, and expect to capture the mutual influence between users' behaviors in QAC and click sessions.
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ItemExtending low-rank matrix factorizations for emerging applications(Georgia Institute of Technology, 2013-08-12) Zhou, KeLow-rank matrix factorizations have become increasingly popular to project high dimensional data into latent spaces with small dimensions in order to obtain better understandings of the data and thus more accurate predictions. In particular, they have been widely applied to important applications such as collaborative filtering and social network analysis. In this thesis, I investigate the applications and extensions of the ideas of the low-rank matrix factorization to solve several practically important problems arise from collaborative filtering and social network analysis. A key challenge in recommendation system research is how to effectively profile new users, a problem generally known as \emph{cold-start} recommendation. In the first part of this work, we extend the low-rank matrix factorization by allowing the latent factors to have more complex structures --- decision trees to solve the problem of cold-start recommendations. In particular, we present \emph{functional matrix factorization} (fMF), a novel cold-start recommendation method that solves the problem of adaptive interview construction based on low-rank matrix factorizations. The second part of this work considers the efficiency problem of making recommendations in the context of large user and item spaces. Specifically, we address the problem through learning binary codes for collaborative filtering, which can be viewed as restricting the latent factors in low-rank matrix factorizations to be binary vectors that represent the binary codes for both users and items. In the third part of this work, we investigate the applications of low-rank matrix factorizations in the context of social network analysis. Specifically, we propose a convex optimization approach to discover the hidden network of social influence with low-rank and sparse structure by modeling the recurrent events at different individuals as multi-dimensional Hawkes processes, emphasizing the mutual-excitation nature of the dynamics of event occurrences. The proposed framework combines the estimation of mutually exciting process and the low-rank matrix factorization in a principled manner. In the fourth part of this work, we estimate the triggering kernels for the Hawkes process. In particular, we focus on estimating the triggering kernels from an infinite dimensional functional space with the Euler Lagrange equation, which can be viewed as applying the idea of low-rank factorizations in the functional space.
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ItemPersonalized search and recommendation for health information resources(Georgia Institute of Technology, 2012-08-24) Crain, Steven P.Consumers face several challenges using the Internet to fill health-related needs. (1) In many cases, they face a language gap as they look for information that is written in unfamiliar technical language. (2) Medical information in social media is of variable quality and may be appealing even when it is dangerous. (3) Discussion groups provide valuable social support for necessary lifestyle changes, but are variable in their levels of activity. (4) Finding less popular groups is tedious. We present solutions to these challenges. We use a novel adaptation of topic models to address the language gap. Conventional topic models discover a set of unrelated topics that together explain the combinations of words in a collection of documents. We add additional structure that provides relationships between topics corresponding to relationships between consumer and technical medical topics. This allows us to support search for technical information using informal consumer medical questions. We also analyze social media related to eating disorders. A third of these videos promote eating disorders and consumers are twice as engaged by these dangerous videos. We study the interactions of two communities in a photo-sharing site. There, a community that encourages recovery from eating disorders interacts with the pro-eating disorder community in an attempt to persuade them, but we found that this attempt entrenches the pro-eating disorder community more firmly in its position. We study the process by which consumers participate in discussion groups in an online diabetes community. We develop novel event history analysis techniques to identify the characteristics of groups in a diabetes community that are correlated with consumer activity. This analysis reveals that uniformly advertise the popular groups to all consumers impairs the diversity of the groups and limits their value to the community. To help consumers find interesting discussion groups, we develop a system for personalized recommendation for social connections. We extend matrix factorization techniques that are effective for product recommendation so that they become suitable for implicit power-law-distributed social ratings. We identify the best approaches for recommendation of a variety of social connections involving consumers, discussion groups and discussions.