splink-graph 0.4.2

a small set of graph functions to be used from pySpark on top of networkx and graphframes

splink_graph

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splink_graph is a small graph utility library meant to be used in the Apache Spark environment, that works with graph data structures such as the ones created from the outputs of data linking processes (candicate pair results) of

The main aim of splink_graph is to offer a small set of functions that work on top of established graph packages like graphframes and networkx , that can help with the process of graph analysis of the output of probabilistic data linkage tools.

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Functionality offered

For a primer on the terminology used please look at TERMINOLOGY.md file in this repo

Cluster metrics

Cluster metrics usually have as an input a spark edgelist dataframe that also includes the component_id (cluster_id) where the edge is in. The output is a row of one or more metrics per cluster

Cluster metrics currently offered:

• diameter
• transitivity
• cluster triangle clustering coeff
• cluster square clustering coeff
• cluster node connectivity
• edge connectivity
• cluster efficiency
• cluster modularity
• cluster avg edge betweenness
• cluster weisfeiler lehman graphhash

Cluster metrics are really helpful at finding the needle (of for example clusters with possible linking errors) in the haystack (whole set of clusters after the data linking process)

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Node metrics

Node metrics have as an input a spark edgelist dataframe that also includes the component_id (cluster_id) where the edge is in. The output is a row of one or more metrics per node

Node metrics curretnly offered:

• Eigenvector Centrality
• Harmonic centrality

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Edge metrics

Edge metrics have as an input a spark edgelist dataframe that also includes the component_id (cluster_id) where the edge is in. The output is a row of one or more metrics per edge

Edge metrics curretnly offered:

• Edge Betweeness
• Bridge Edges

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Contributing

Feel free to contribute by

• Forking the repository to suggest a change, and/or
• Starting an issue.