Wellesley College. (2022, February 3). Missing the bar: How people misinterpret data in bar graphs. ScienceDaily. Retrieved June 2, 2025 from www.sciencedaily.com / releases / 2022 / 02 ...

This paper introduces a constraint-based approach to clarify missing values in graphs. Our method capitalizes on a set Σ of graph data constraints. An explanation is a sequence of operational ...

Today, graphs are used in a wide variety of government contexts. Graph databases have been deployed to address everything from managing global pandemics, improving urban planning and preventing fraud, ...

Spatiotemporal graphs are often highly sparse, with time series characterized by multiple, concurrent, and even long sequences of missing data, e.g., due to the unreliable underlying sensor network.

In June 2023, Gartner researchers said, data and analytics leaders must leverage the power of LLMs with the robustness of knowledge graphs for fault-tolerant AI applications.

In real-world network scenarios, modal absence may be caused by various factors, such as sensor damage, data corruption, and human errors in recording. Effectively integrating multimodal missing data ...

This is different than the "No Data" property in Options, as it is not the entire graph that is missing data. (unless I am missing something here!) So for example, let's say that you have a chart that ...

Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.