Abstract
We propose an overlapping community model based on the Affiliation Graph Model (AGM), that exhibits the pluralistic homophily property that the probability of a link between nodes increases with increasing number of shared communities. We take inspiration from the Mixed Membership Stochastic Blockmodel (MMSB), in proposing an edgewise community affiliation. This allows decoupling of community affiliations between nodes, opening the way to scalable inference. We show that our model corresponds to an AGM with soft community affiliations and develop a scalable algorithm based on a Stochastic Gradient Riemannian Langevin Dynamics (SGRLD) sampler. Empirical results show that the model can scale to network sizes that are beyond the capabilities of MCMC samplers of the standard AGM. We achieve comparable performance in terms of accuracy and run-time efficiency to scalable MMSB samplers.
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This project has been funded by Science Foundation Ireland under Grant No. SFI/12/RC/2289.
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Laitonjam, N., Huáng, W., Hurley, N.J. (2020). A Soft Affiliation Graph Model for Scalable Overlapping Community Detection. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11906. Springer, Cham. https://doi.org/10.1007/978-3-030-46150-8_30
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