MINDS Seminar Series | Hanbaek Lyu (University of Wisconsin) - Learning low-rank latent mesoscale structures in networks
| Date |
2024-01-17 ~ 2024-01-17 |
Time |
16:00:00 ~ 17:00:00 |
| Speaker |
Hanbaek Lyu |
Affiliation |
University of Wisconsin |
| Place |
Math Bldg 404 & Online streaming (Zoom) |
Streaming link |
ID : 688 896 1076 /
PW : 54321 |
| Topic |
Learning low-rank latent mesoscale structures in networks |
| Contents |
Researchers in many fields use networks to represent interactions between entities in complex systems. To study the large-scale behavior of complex systems it is useful to examine mesoscale structures in networks as building blocks that influence such behavior. In this paper we present an approach to describe low-rank mesoscale structures in networks. We find that many real-world networks possess a small set of latent motifs that effectively approximate most subgraphs at a fixed mesoscale. Such low-rank mesoscale structures allow one to reconstruct networks by approximating subgraphs of a network using combinations of latent motifs. Employing subgraph sampling and nonnegative matrix factorization enables the discovery of these latent motifs. The ability to encode and reconstruct networks using a small set of latent motifs has many applications in network analysis including network comparison network denoising and edge inference. |