Graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science. While the objective of most graph clustering algorithms is to find a vertex set of low conductance, there has been a sequence of recent studies that highlight the importance of the inter-connection between clusters when analysing real-world datasets. Following this line of research, in this work we study bipartite-like clusters and present efficient and online algorithms that find such clusters in both undirected graphs and directed ones. We conduct experimental studies on both synthetic and real-world datasets, and show that our algorithms significantly speedup the running time of existing clustering algorithms while preserving their effectiveness.

Abstract Incident report triage is a common endeavor in many industry sectors, often coupled with serious public safety implications. For example, at the US Food and Drug Administration (FDA), analysts triage an influx of incident reports to identify previously undiscovered drug safety problems. However, these analysts currently conduct this critical yet error‐prone incident report triage using a generic table‐based interface, with no formal support. Visualization design, task‐characterization methodologies, and evaluation models offer several possibilities for better supporting triage workflows, including those dealing with drug safety and beyond. In this work, we aim to elevate the work of triage through a task‐abstraction activity with FDA analysts. Second, we design an alternative gist‐based summary of text documents used in triage (SumRe). Third, we conduct a crowdsourced evaluation of SumRe with medical experts. Results of the crowdsourced study with medical experts (n = 20) suggest that SumRe better supports accuracy in understanding the gist of a given report, and in identifying important reports for followup activities. We discuss implications of these results, including design considerations for triage workflows beyond the drug domain, as well as methodologies for comparing visualization‐enabled text summaries.

Abstract Adverse reactions caused by drug‐drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post‐marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e., exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DTVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain‐specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in‐depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case‐studies and interviews by drug analysts at the US Food and Drug Administration ‐ both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug‐drug interactions.

Drug-drug interaction related adverse events (DIAE) signals are a major public health issue. Drug safety analysts must sift through thousands of adverse event reports submitted daily to U.S. Food and Drug Administration (FDA) to discover unexpected DIAE signals, which if addressed can lead to life-saving actions. To facilitate the DIAE discovery from these massive data sets, we design several technological innovations that together are integrated into an interactive visual analytics system called DEVES 1. First, our state- of-the-art DIAE mining algorithm efficiently infers potential DIAE signals from these reports, and then ranks them based on their significance score. For interpretability of these inferred DIAE signals, domain knowledge of adverse events and already known drug interactions is extracted from external authoritative data sources and then seamlessly integrated with the inferred signal set. Guided by this augmented signal model, DEVES supports advanced signal analytics - empowering the analyst to interact with linked visualizations offering complementary perspectives into the signal set and its supporting evidence in the form of reports. Our demonstration showcases the technological innovations of DEVES using real-world FDA datasets, demonstrating that DEVES effectively supports the core regulatory tasks from signal screening, signal prioritization to signal validation.

Adverse drug reactions (ADRs) caused by drug-drug interactions (DDI) are a major cause of morbidity and mortality worldwide. There is a growing need for computing-supported methods that facilitate the automated signaling of DDI related ADRs (DIARs) that otherwise would remain undiscovered in millions of ADR reports. In this demonstration, we showcase our MeDIAR technology - an end-to-end DIAR signal generation, exploration and validation solution for pharmaceutical regulatory agencies to detect true DIAR signals from a drug surveillance database. MeDIAR's innovations include an efficient rule-driven learning algorithm for deriving DIAR signals from ADR reports, an innovative scoring methodology based on the proposed contextual association cluster model to rank the generated signals by their importance. Further, these ranked signals are augmented with meta information such as their significance level and their severity, along with links to their supporting ADR reports. Lastly, MeDIAR features an interactive visual analytics interface to support drug safety evaluators in reviewing and discovering unknown severe DIARs.