How can you clean complex data structures like graphs and networks?

1 Identify data quality issues

Data cleaning begins with identifying the data quality issues that affect your graphs and networks, such as missing or incomplete data, duplicate or inconsistent data, outliers or anomalies, and irrelevant or noisy data. These issues can be caused by errors in data collection or integration, and can lead to extreme or unusual values or patterns. It is important to identify and remove these issues to avoid introducing bias or noise to your results.

2 Explore and visualize data

Exploring and visualizing your graphs and networks is an essential step for data cleaning, to gain a deeper understanding of their structure, properties, and patterns. Python libraries such as NetworkX, igraph, or graph-tool can create and manipulate graphs and networks, while Matplotlib, Plotly, or Bokeh can be used to plot and interact with them. Node-link diagrams illustrate the nodes and edges of the graph or network, while adjacency matrices show the presence or absence of edges between pairs of nodes. Centrality measures indicate the importance or influence of nodes or edges in the graph or network. Additionally, community detection algorithms like modularity, Louvain, or Girvan-Newman can be used to detect clusters or groupings of nodes or edges in the graph or network.

3 Handle missing or incomplete data

Imputing or estimating missing values or attributes in graphs and networks is possible with Python libraries like sklearn, fancyimpute, or datawig. Mean, median, or mode imputation can replace the missing values with the mean, median, or mode of the corresponding attribute or label. K-nearest neighbors imputation can use the weighted average of the k nearest neighbors of the corresponding node or edge. Matrix factorization imputation decomposes the adjacency matrix of the graph into two lower-dimensional matrices to reconstruct the missing values. Graph neural network imputation uses a deep learning model to learn features and representations of nodes and edges to predict the missing values.

4 Remove duplicate or inconsistent data

When dealing with duplicate or inconsistent data in graphs and networks, you can remove or merge the duplicate or conflicting nodes or edges, based on some criteria or rules. Python libraries such as pandas, dedupe, or recordlinkage can be used to apply different removal or merging methods, such as exact matching, fuzzy matching, and rule-based matching. For exact matching, this method identifies and removes nodes or edges that have exactly the same attributes or labels, or merges them into one node or edge with the same attributes or labels. Fuzzy matching identifies and removes nodes or edges that have similar but not identical attributes or labels based on some threshold or distance measure, or merges them into one node or edge with the average or most frequent attributes or labels. Rule-based matching identifies and removes nodes or edges that have conflicting or contradictory attributes or labels based on some logic or domain knowledge, or merges them into one node with the resolved attributes or labels.

5 Detect and treat outliers or anomalies

A third way to handle outliers or anomalies in graphs and networks is to detect and treat them using Python libraries such as scipy, sklearn, or pyod. This can be done through statistical tests, which test the hypothesis that the values or patterns of the nodes or edges are from a certain distribution or model and reject the ones that have a low probability or a high deviation. Clustering methods group the nodes or edges into clusters based on some similarity or distance measure, while isolation forest methods isolate the nodes or edges by randomly splitting the feature space. In both cases, the outliers are identified by their short path length, high isolation score, or distance from cluster centers.

6 Filter or transform irrelevant or noisy data

A fourth way to handle irrelevant or noisy data in graphs and networks is to filter or transform them, based on some criteria or goals. Python libraries such as numpy, scipy, or sklearn can be used to apply various filtering or transforming methods, such as feature selection, feature extraction, and feature scaling. Feature selection selects the most relevant or informative attributes or labels of the nodes or edges, while feature extraction extracts new features or representations of the nodes or edges. Feature scaling scales or normalizes the values or ranges of the attributes or labels of the nodes or edges. These methods are based on criteria such as correlation, variance, mutual information, chi-square test, principal component analysis, singular value decomposition, graph embedding, min-max scaling, standardization, and robust scaling.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?