Exploring similar nodes in attributed networks represents a key challenge in data mining. While recent representation learning methods embed networks into low-dimensional vectors, they often implicitly assume a uniform and continuous feature space. This paper proposes a visual analytics approach using dimensionality reduction to help clarify the true topological structure of high-dimensional feature spaces formed by nodes' neighborhood attribute profiles. Analyzing inter-firm transaction networks indicates that structural roles can form complex, non-linear manifolds with density biases. Comparing this feature space with industry classifications suggested: (1) supply chain hierarchies transition continuously; (2) categories treated identically under general semantics can be clearly separated by actual transaction networks; and (3) a single industry label may fragment into multiple regions. These findings suggest potential limitations in assuming identical semantics imply similar structural roles and highlight the possible need for new similarity metrics aligned with manifold topology.
Personality recognition has traditionally been constrained by theory-dependent formulations, where models are trained to fit predefined psychological taxonomies rather than uncovering shared underlying behavioral structure. This limits generalization, as personality itself is bet…
We introduce UISTful, a system that turns reading activity into a collective portrait of a scholarly community. Readers explore a semantic globe of UIST papers and authors while the system records private reading traces that can be reviewed, reflected upon, curated, and published…
We investigate the effectiveness of interventions that reduce the visibility of offensive content on the local social platform Nextdoor. Content filtering -- hiding or downranking offensive content that brushes against a platform's rules without clearly breaking them -- is deploy…
Climate change is a source of anxiety about the future. Understanding how people express themselves about climate change enables us to address such concerns. To study climate change expression on social media, we analyzed 200 TikTok videos tagged with #climatechange, identifying…
Composition in Traditional Chinese Paintings (TCPs) carries spatial, narrative, and cultural-aesthetic meaning. Systematic compositional analysis is therefore important for understanding their visual language and artistic meaning. Traditional compositional analysis is mainly qual…
Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and inte…