Currently, I have a dataset with pairs . The idea is to detect any anomaly in these relationships. I was able to just use pandas to do the analysis so far.
|C1..............| A1......| 2hrs.......|23 |
As an example above, you can project the bipartite nodes into Agent nodes. You get a normal graph then. All Agents which share a common Customer, form cliques, which you can easily get just using pandas operations in the original graph.
Other than this, is there any worthwhile algorithms to run ? Or any links will be appreciated as well.
Btw, I have good experience working with many unipartite graphs (normal), where I have run many types of algorithms (centrality measures, clique/communities, connected components etc) and social network analysis.
Edit : Just to confirm, my question is not to get the subgraph or write any new algorithms. I am just going to be using Networkx to achieve this. However, I am stuck at what are the analytics possibilities with bipartite graphs ?
Edit : I am digging through the web. And add the resources here, as I find them. Once I find something useful, I will add a solution to my question. 🤞
- Networkx - Lists all bipartite graph algorithms