VIGOR: Interactive Visual Exploration of Graph Query Results.

Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR's ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors.

VISAGE: Interactive Visual Graph Querying.

Extracting useful patterns from large network datasets has become a fundamental challenge in many domains. We present VISAGE, an interactive visual graph querying approach that empowers users to construct expressive queries, without writing complex code (e.g., finding money laundering rings of bankers and business owners). Our contributions are as follows: (1) we introduce graph autocomplete, an interactive approach that guides users to construct and refine queries, preventing over-specification; (2) VISAGE guides the construction of graph queries using a data-driven approach, enabling users to specify queries with varying levels of specificity, from concrete and detailed (e.g., query by example), to abstract (e.g., with "wildcard" nodes of any types), to purely structural matching; (3) a twelve-participant, within-subject user study demonstrates VISAGE's ease of use and the ability to construct graph queries significantly faster than using a conventional query language; (4) VISAGE works on real graphs with over 468K edges, achieving sub-second response times for common queries.

Characterizing Smoking and Drinking Abstinence from Social Media.

Social media has been established to bear signals relating to health and well-being states. In this paper, we investigate the potential of social media in characterizing and understanding abstinence from tobacco or alcohol use. While the link between behavior and addiction has been explored in psychology literature, the lack of longitudinal self-reported data on long-term abstinence has challenged addiction research. We leverage the activity spanning almost eight years on two prominent communities on Reddit: StopSmoking and StopDrinking. We use the self-reported "badge" information of nearly a thousand users as gold standard information on their abstinence status to characterize long-term abstinence. We build supervised learning based statistical models that use the linguistic features of the content shared by the users as well as the network structure of their social interactions. Our findings indicate that long-term abstinence from smoking or drinking (~one year) can be distinguished from short-term abstinence (~40 days) with 85% accuracy. We further show that language and interaction on social media offer powerful cues towards characterizing these addiction-related health outcomes. We discuss the implications of our findings in social media and health research, and in the role of social media as a platform for positive behavior change and therapy.

Interactive Querying over Large Network Data: Scalability, Visualization, and Interaction Design.

Given the explosive growth of modern graph data, new methods are needed that allow for the querying of complex graph structures without the need of a complicated querying languages; in short, interactive graph querying is desirable. We describe our work towards achieving our overall research goal of designing and developing an interactive querying system for large network data. We focus on three critical aspects: scalable data mining algorithms, graph visualization, and interaction design. We have already completed an approximate subgraph matching system called MAGE in our previous work that fulfills the algorithmic foundation allowing us to query a graph with hundreds of millions of edges. Our preliminary work on visual graph querying, Graphite, was the first step in the process to making an interactive graph querying system. We are in the process of designing the graph visualization and robust interaction needed to make truly interactive graph querying a reality.

Understanding variations in pediatric asthma care processes in the emergency department using visual analytics.

Health care delivery processes consist of complex activity sequences spanning organizational, spatial, and temporal boundaries. Care is human-directed so these processes can have wide variations in cost, quality, and outcome making systemic care process analysis, conformance testing, and improvement challenging. We designed and developed an interactive visual analytic process exploration and discovery tool and used it to explore clinical data from 5784 pediatric asthma emergency department patients.

MAGE: Matching Approximate Patterns in Richly-Attributed Graphs.

Given a large graph with millions of nodes and edges, say a social network where both its nodes and edges have multiple attributes (e.g., job titles, tie strengths), how to quickly find subgraphs of interest (e.g., a ring of businessmen with strong ties)? We present MAGE, a scalable, multicore subgraph matching approach that supports expressive queries over large, richly-attributed graphs. Our major contributions include: (1) MAGE supports graphs with both node and edge attributes (most existing approaches handle either one, but not both); (2) it supports expressive queries, allowing multiple attributes on an edge, wildcards as attribute values (i.e., match any permissible values), and attributes with continuous values; and (3) it is scalable, supporting graphs with several hundred million edges. We demonstrate MAGE's effectiveness and scalability via extensive experiments on large real and synthetic graphs, such as a Google+ social network with 460 million edges.

Anonymization of longitudinal electronic medical records.

Electronic medical record (EMR) systems have enabled healthcare providers to collect detailed patient information from the primary care domain. At the same time, longitudinal data from EMRs are increasingly combined with biorepositories to generate personalized clinical decision support protocols. Emerging policies encourage investigators to disseminate such data in a deidentified form for reuse and collaboration, but organizations are hesitant to do so because they fear such actions will jeopardize patient privacy. In particular, there are concerns that residual demographic and clinical features could be exploited for reidentification purposes. Various approaches have been developed to anonymize clinical data, but they neglect temporal information and are, thus, insufficient for emerging biomedical research paradigms. This paper proposes a novel approach to share patient-specific longitudinal data that offers robust privacy guarantees, while preserving data utility for many biomedical investigations. Our approach aggregates temporal and diagnostic information using heuristics inspired from sequence alignment and clustering methods. We demonstrate that the proposed approach can generate anonymized data that permit effective biomedical analysis using several patient cohorts derived from the EMR system of the Vanderbilt University Medical Center.

Anonymization of administrative billing codes with repeated diagnoses through censoring.

Patient-specific data from electronic medical records (EMRs) is increasingly shared in a de-identified form to support research. However, EMRs are susceptible to noise, error, and variation, which can limit their utility for reuse. One way to enhance the utility of EMRs is to record the number of times diagnosis codes are assigned to a patient when this data is shared. This is, however, challenging because releasing such data may be leveraged to compromise patients' identity. In this paper, we present an approach that, to the best of our knowledge, is the first that can prevent re-identification through repeated diagnosis codes. Our method transforms records to preserve privacy while retaining much of their utility. Experiments conducted using 2676 patients from the EMR system of the Vanderbilt University Medical Center verify that our method is able to retain an average of 95.4% of the diagnosis codes in a common data sharing scenario.