2 research outputs found
RekomGNN: Visualizing, Contextualizing and Evaluating Graph Neural Networks Recommendations
Content recommendation tasks increasingly use Graph Neural Networks, but it
remains challenging for machine learning experts to assess the quality of their
outputs. Visualization systems for GNNs that could support this interrogation
are few. Moreover, those that do exist focus primarily on exposing GNN
architectures for tuning and prediction tasks and do not address the challenges
of recommendation tasks. We developed RekomGNN, a visual analytics system that
supports ML experts in exploring GNN recommendations across several dimensions
and making annotations about their quality. RekomGNN straddles the design space
between Neural Network and recommender system visualization to arrive at a set
of encoding and interaction choices for recommendation tasks. We found that
RekomGNN helps experts make qualitative assessments of the GNN's results, which
they can use for model refinement. Overall, our contributions and findings add
to the growing understanding of visualizing GNNs for increasingly complex
tasks
PIXAL: Anomaly Reasoning with Visual Analytics
Anomaly detection remains an open challenge in many application areas. While
there are a number of available machine learning algorithms for detecting
anomalies, analysts are frequently asked to take additional steps in reasoning
about the root cause of the anomalies and form actionable hypotheses that can
be communicated to business stakeholders. Without the appropriate tools, this
reasoning process is time-consuming, tedious, and potentially error-prone. In
this paper we present PIXAL, a visual analytics system developed following an
iterative design process with professional analysts responsible for anomaly
detection. PIXAL is designed to fill gaps in existing tools commonly used by
analysts to reason with and make sense of anomalies. PIXAL consists of three
components: (1) an algorithm that finds patterns by aggregating multiple
anomalous data points using first-order predicates, (2) a visualization tool
that allows the analyst to build trust in the algorithmically-generated
predicates by performing comparative and counterfactual analyses, and (3) a
visualization tool that helps the analyst generate and validate hypotheses by
exploring which features in the data most explain the anomalies. Finally, we
present the results of a qualitative observational study with professional
analysts. These results of the study indicate that PIXAL facilitates the
anomaly reasoning process, allowing analysts to make sense of anomalies and
generate hypotheses that are meaningful and actionable to business
stakeholders