This is a library built on top D3 with the goal of improving how we work with large and messy graphs. It extends the notion of nodes and links with groups of nodes. This is useful when multiple nodes are in fact the same thing or belong to the same group.

2025年1月27日 · To tackle the problem of noisy heterogeneous graphs, a new approach called NoiseHGNN was created. This model is designed specifically for learning from these messy connections. It's like equipping a detective with a magnifying glass to find hidden clues in a …

2023年3月30日 · The only thing you’re missing is empty files (leftover as it were) that are still referenced somewhere in your graph. If they’re not referenced, then through all pages => three dots menu => remove orphaned pages, Logseq can delete them for you.

Learn how to work around problems with visualizing messy and missing data. Data visualization tutorials generally use pre-processed data. But what about datasets in the wild? What do we do about missing data? Or outliers that largely skew visualizations? What do we do when there are too many observations to be interpretable in a scatterplot?

Misleading graphs are sometimes deliberately misleading and sometimes it’s just a case of people not understanding the data behind the graph they create. The “classic” types of misleading graphs include cases where: The Vertical scale is too big or too small, or skips numbers, or doesn’t start at zero. The graph isn’t labeled properly.

A line graph quickly becomes a jumbled mess with 100 lines. Is there a better type of graph I can use to display this information? Or should I look at being able to toggle individual lines on/off?

2022年9月7日 · Unclear data and messy graphs When a bar graph or a donut chart jumps at you with too many lines, colors, and parameters, it confuses and looks disorganized. Just imagine that you made a website that represents your real estate business, and you need to know the statistics on how many users visit your site each day and which countries they are ...

Figure 4.1: More often than not, raw data looks messy. In this chapter you’ll learn about different tools, in order to help you make decisions about which one to use to clean up your data efficiently.

In this chapter, we looked at cleaning up tables in Google Sheets, liberating tabular data trapped in PDFs using Tabula, and using OpenRefine to clean up very messy datasets. You’ll often find yourself using several of these tools on the same dataset before it …

If your data set has more than 100 or so values, a scatter plot becomes messy. Alternatives are to show a box-and-whiskers plot, a frequency distribution (histogram), or a cumulative frequency distribution. If you are plotting XY data, especially with multiple treatment groups, plotting every replicate can lead to a messy graph.