Robert Pienta

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant works from multiple research fields - data mining, machine learning, human-computer ineraction, information visualization, information retrieval, and recommender systems - to underline their parallel and complementary contributions to graph sensemaking. We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global). We summarize and compare their strengths and weaknesses, and highlight open challenges. We conclude with future research directions for graph sensemaking.

On the parallel simulation of scale-free networks

Scale-free networks have received much attention in recent years due to their prevalence in many important applications such as social networks, biological systems, and the Internet. We consider the use of conservative parallel discrete event simulation techniques in network simulation applications involving scale-free networks. An analytical model is developed to study the parallelism available in simulations using a conservative time window synchronization algorithm. The performance of scale-free network simulations using two variants of the Chandy/Misra/Bryant synchronization algorithm are evaluated. These results demonstrate the importance of topology in the performance of synchronization protocols when developing parallel discrete event simulations involving scale-free networks, and highlight important challenges such as performance bottlenecks that must be addressed to achieve efficient parallel execution. These results suggest that new approaches to parallel simulation of scale-free networks may offer significant benefit.

A Comparative Analysis of Thermal and Visual Modalities for Automated Facial Expression Recognition

Facial expressions are formed through complicated muscular actions and can be taxonomized using the Facial Action Coding System (FACS). FACS breaks down human facial expressions into discreet action units (AUs) and often combines them together to form more elaborate expressions. In this paper, we present a comparative analysis of performance of automated facial expression recognition from thermal facial videos, visual facial videos, and their fusion. The feature extraction process consists of first placing regions of interest (ROIs) at 13 fiducial regions on the face that are critical for evaluating all action units, then extracting mean value in each of the ROIs, and finally applying principal component analysis (PCA) to extract the deviation from neutral expression at each of the corresponding ROIs. To classify facial expressions, we train a feed-forward multilayer perceptron with the standard deviation expression profiles obtained from the feature extraction stage. Our experimental results depicts that the thermal imaging modality outperforms visual modality, and hence overcomes some of the shortcomings usually noticed in the visual domain due to illumination and skin complexion variations. We have also shown that the decision level fusion of thermal and visual expression classification algorithms gives better results than either of the individual modalities.

Visual Graph Query Construction and Refinement

Locating and extracting subgraphs from large network datasets is a challenge in many domains, one that often requires learning new querying languages. We will present the first demonstration of VISAGE, an interactive visual graph querying approach that empowers analysts to construct expressive queries, without writing complex code (see our video: https://youtu.be/l2L7Y5mCh1s). VISAGE guides the construction of graph queries using a data-driven approach, enabling analysts to specify queries with varying levels of specificity, by sampling matches to a query during the analysts interaction. We will demonstrate and invite the audience to try VISAGE on a popular film-actor-director graph from Rotten Tomatoes.

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 VIGORs 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.

FACETS: Adaptive Local Exploration of Large Graphs

Visualization is a powerful paradigm for exploratory data analysis. Visualizing large graphs, however, often results in excessive edges crossings and overlapping nodes. We propose a new scalable approach called FACETS that helps users adaptively explore large million-node graphs from a local perspective, guiding them to focus on nodes and neighborhoods that are most subjectively interesting to users. We contribute novel ideas to measure this interestingness in terms of how surprising a neighborhood is given the background distribution, as well as how well it matches what the user has chosen to explore. FACETS uses Jensen-Shannon divergence over information-theoretically optimized histograms to calculate the subjective user interest and surprise scores. Participants in a user study found FACETS easy to use, easy to learn, and exciting to use. Empirical runtime analyses demonstrated FACETS’s practical scalability on large real-world graphs with up to 5 million edges, returning results in fewer than 1.5 seconds.

Identifying Successful Investors in the Startup Ecosystem

Who can spot the next Google, Facebook, or Twitter? Who can discover the next billion-dollar startups? Measuring investor success is a challenging task, as investment strategies can vary widely. We propose InvestorRank, a novel method for identifying successful investors by analyzing how an investors collaboration network change over time. InvestorRank captures the intuition that a successful investor achieves increasingly success in spotting great startups, or is able to keep doing so persistently. Our results show potential in discovering relatively unknown investors that may be the success stories of tomorrow.

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.

Carina: Interactive Million-Node Graph Visualization using Web Browser Technologies

We are working on a scalable, interactive visualization system, called Carina, for people to explore million-node graphs. By using latest web browser technologies, Carina offers fast graph rendering via WebGL, and works across desktop (via Electron) and mobile platforms. Different from most existing graph visualization tools, Carina does not store the full graph in RAM, enabling it to work with graphs with up to 69M edges. We are working to improve and open-source Carina, to offer researchers and practitioners a new, scalable way to explore and visualize large graph datasets.

STEPS: A Spatio-temporal Electric Power Systems Visualization

As the bulk electric grid becomes more complex, power system operators and engineers have more information to process and interpret than ever before. The information overload they experience can be mitigated by effective visualizations that facilitate rapid and intuitive assessment of the system state. With the introduction of non-dispatchable renewable energy, flexible loads, and energy storage, the ability to temporally explore system states becomes critical. This paper introduces STEPS, a new 3D Spatio-temporal Electric Power Systems visualization tool suitable for steady-state operational applications.