Probabilistic Graphical Modeling on Big Data

The rise of Big Data in recent years brings many challenges to modern statistical analysis and modeling. In toxicogenomics, the advancement of high-throughput screening technologies facilitates the generation of massive amount of biological data, a big data phenomena in biomedical science. Yet, researchers still heavily rely on key word search and/or literature review to navigate the databases and analyses are often done in rather small-scale. As a result, the rich information of a database has not been fully utilized, particularly for the information embedded in the interactive nature between data points that are largely ignored and buried. For the past 10 years, probabilistic topic modeling has been recognized as an effective machine learning algorithm to annotate the hidden thematic structure of massive collection of documents. The analogy between text corpus and large-scale genomic data enables the application of text mining tools, like probabilistic topic models, to explore hidden patterns of genomic data and to the extension of altered biological functions. In this study, we developed a generalized probabilistic topic model to analyze a toxicogenomics data set that consists of a large number of gene expression data from the rat livers treated with drugs in multiple dose and time-points. We discovered the hidden patterns in gene expression associated with the effect of doses and time-points of treatment. Finally, we illustrated the ability of our model to identify the evidence of potential reduction of animal use. In online Social network, Social network services have hundreds of millions, sometimes even billions, of monthly active users. These complex and vast Social networks are tremendous resources for understanding the human interactions. Especially, characterizing the strength of Social interactions becomes essential task for researching or marketing Social networks. Instead of traditional dichotomy of strong and weak tie assumption, we believe that there are more types of Social ties than just two. We use cosine similarity to measure the strength of the Social ties and apply incremental Dirichlet process Gaussian mixture model to group tie into different clusters of ties. Comparing to other methods, our approach generates superior accuracy in classification on data with ground truth. The incremental algorithm also allow data to be added or deleted in a dynamic Social network with minimal computer cost. In addition, it has been shown that the network constraints of individuals can be used to predict ones\u27 career successes. Under our multiple type of ties assumption, individuals are profiled based on their surrounding relationships. We demonstrate that network profile of a individual is directly linked to Social significance in real world

Extraction and Analysis of Facebook Friendship Relations

Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms

A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

Community detection with node attributes in multilayer networks

Community detection in networks is commonly performed using information about interactions between nodes. Recent advances have been made to incorporate multiple types of interactions, thus generalizing standard methods to multilayer networks. Often, though, one can access additional information regarding individual nodes, attributes, or covariates. A relevant question is thus how to properly incorporate this extra information in such frameworks. Here we develop a method that incorporates both the topology of interactions and node attributes to extract communities in multilayer networks. We propose a principled probabilistic method that does not assume any a priori correlation structure between attributes and communities but rather infers this from data. This leads to an efficient algorithmic implementation that exploits the sparsity of the dataset and can be used to perform several inference tasks; we provide an open-source implementation of the code online. We demonstrate our method on both synthetic and real-world data and compare performance with methods that do not use any attribute information. We find that including node information helps in predicting missing links or attributes. It also leads to more interpretable community structures and allows the quantification of the impact of the node attributes given in input

Declarative Cleaning, Analysis, and Querying of Graph-structured Data

Much of today's data including social, biological, sensor, computer, and transportation network data is naturally modeled and represented by graphs. Typically, data describing these networks is observational, and thus noisy and incomplete. Therefore, methods for efficiently managing graph-structured data of this nature are needed, especially with the abundance and increasing sizes of such data. In my dissertation, I develop declarative methods to perform cleaning, analysis and querying of graph-structured data efficiently. For declarative cleaning of graph-structured data, I identify a set of primitives to support the extraction and inference of the underlying true network from observational data, and describe a framework that enables a network analyst to easily implement and combine new extraction and cleaning techniques. The task specification language is based on Datalog with a set of extensions designed to enable different graph cleaning primitives. For declarative analysis, I introduce 'ego-centric pattern census queries', a new type of graph analysis query that supports searching for structural patterns in every node's neighborhood and reporting their counts for further analysis. I define an SQL-based declarative language to support this class of queries, and develop a series of efficient query evaluation algorithms for it. Finally, I present an approach for querying large uncertain graphs that supports reasoning about uncertainty of node attributes, uncertainty of edge existence, and a new type of uncertainty, called identity linkage uncertainty, where a group of nodes can potentially refer to the same real-world entity. I define a probabilistic graph model to capture all these types of uncertainties, and to resolve identity linkage merges. I propose 'context-aware path indexing' and 'join-candidate reduction' methods to efficiently enable subgraph matching queries over large uncertain graphs of this type