Abstract
Modularity Vitality has recently been identified as an efficient and powerful way of identifying community bridges and community hubs in unipartite networks. In this work, we expand this line of analysis to bipartite networks by deriving efficient calculations for Bipartite Modularity Vitality, Modularity Vitality on projected networks over the projected nodes, and Modularity Vitalityon projected networks over the non-projected nodes. These measures of contribution to community structure aid in the identification of central nodes in bipartite networks, and in the interpretation of bipartite communities.
Tom Magelinski, PhD
Senior Data Scientist - Information Extraction and Generative AI
I build AI systems that help domain experts understand vast amounts of data through state-of-the-art techniques from natural language processing, generative modeling, graph ML, and network science. I’m particularly interested researching and developing techniques to combine NLP and graph-based approaches to capture complex relationships in unstructured data.