Infinite multiple membership relational modeling for complex networks

By Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen. 2011

64 citations recorded by the source corpus.

Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary to existing multiple-membership models that scale quadratically in the number of vertices the proposed model scales linearly in the number of links admitting multiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show on “real” size benchmark network data that accounting for multiple memberships improves the learning of latent structure as measured by link prediction while explicitly accounting for multiple membership result in a more compact representation of the latent structure of networks.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jure Leskovec
2. Julian McAuley
3. David M. Blei
4. Charu C. Aggarwal
5. Percy Liang
6. David Mimno
7. Aditya Krishna Menon
8. Qiang Liu

In Statistical and Nonlinear Physics

1. Jure Leskovec
2. Julian McAuley
3. Jaewon Yang
4. Prem Gopalan
5. David M. Blei
6. Rok Sosic
7. David Mimno
8. Charles Peter Elkan

In Signal Processing

1. Jure Leskovec
2. David Mimno
3. Charles Peter Elkan
4. David M. Blei
5. Prem Gopalan
6. Aditya Krishna Menon
7. Michael J. Freedman
8. Jaewon Yang

Citations in the corpus

• Overlapping Communities Explain Core–Periphery Organization of Networks (2014, methodology): “[43] developed a non-parametric Bayesian multiple membership latent feature model for networks where edges of the network are generated by using a “soft-OR” function.”
• Link Prediction via Matrix Factorization (2011, background): “Latent class IBP [24] Optional No No IMRM [ 25 ] Optional No No”
• Scalable Inference of Overlapping Communities (2012, background): “Recently, other researchers have proposed latent feature network models [20, 21] that compute the probabilities of links based on the interactions between binary features associated with each node.”
• Community Detection in Networks with Node Attributes (2013, background): “This means that the number of operations required to compute the gradient is proportional to the degr e of nodeu since [30], [41]:”