Cited By
View all- Pietrasik MReformat M(2021)Neural Blockmodeling for Multilayer Networks2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533909(1-8)Online publication date: 18-Jul-2021
Deep Dynamic Mixed Membership Stochastic Blockmodel
Pages 141 - 148
Abstract
Latent community models are successful at statistically modeling network data by assigning network entities to communities and modelling entity relations as the relations of their communities. In this paper, we describe the limitation of these models in inferring relations between two communities when the entity relations between these communities are unobserved. We propose a solution to this problem by factorizing the community relations matrix into two community feature matrices, thereby adding a dependency between community relations. We introduce the deep dynamic mixed membership stochastic blockmodel based network (DDBN) to demonstrate the feasibility of such an approach. Our model marries the mixed membership stochastic blockmodel (MMSB) with deep neural networks for rich feature extraction and introduces a temporal dependency in latent features using a long short-term memory unit for dynamic network modeling. We evaluate our model on the link prediction task in static and dynamic networks and find that our model achieves comparable results with state-of-the-art methods.
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Index Terms
- Deep Dynamic Mixed Membership Stochastic Blockmodel
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Published In
October 2019
507 pages
ISBN:9781450369343
DOI:10.1145/3350546
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Association for Computing Machinery
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Published: 14 October 2019
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WI '19
WI '19: IEEE/WIC/ACM International Conference on Web Intelligence
October 14 - 17, 2019
Thessaloniki, Greece
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Overall Acceptance Rate 118 of 178 submissions, 66%
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View all- Pietrasik MReformat M(2021)Neural Blockmodeling for Multilayer Networks2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533909(1-8)Online publication date: 18-Jul-2021
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