Manifold regularized stochastic block model
Stochastic block models (SBMs) play essential roles in network analysis, especially in those related to unsupervised learning (clustering). Many SBM-based approaches have been proposed to uncover network clusters, by means of maximizing the block-wise posterior probability that generates edges bridg...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/147803 |
| _version_ | 1824454113181564928 |
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| author | He, Tiantian Bai, Lu Ong, Yew-Soon |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering He, Tiantian Bai, Lu Ong, Yew-Soon |
| author_sort | He, Tiantian |
| collection | NTU |
| description | Stochastic block models (SBMs) play essential roles in network analysis, especially in those related to unsupervised learning (clustering). Many SBM-based approaches have been proposed to uncover network clusters, by means of maximizing the block-wise posterior probability that generates edges bridging vertices. However, none of them is capable of inferring the cluster preference for each vertex through simultaneously modeling block-wise edge structure, vertex features, and similarities between pairwise vertices. To fill this void, we propose a novel SBM dubbed manifold regularized stochastic model (MrSBM) to perform the task of unsupervised learning in network data in this paper. Besides modeling edges that are within or connecting blocks, MrSBM also considers modeling vertex features utilizing the probabilities of vertex-cluster preference and feature-cluster contribution. In addition, MrSBM attempts to generate manifold similarity of pairwise vertices utilizing the inferred vertex-cluster preference. As a result, the inference of cluster preference may well capture the comparability in the manifold. We design a novel process for network data generation, based on which, we specify the model structure and formulate the network clustering problem using a novel likelihood function. To guarantee MrSBM learns the optimal cluster preference for each vertex, we derive an effective Expectation-Maximization based algorithm for model fitting. MrSBM has been tested on five sets of real-world network data and has been compared with both classical and state-of-the-art approaches to network clustering. The competitive experimental results validate the effectiveness of MrSBM. |
| first_indexed | 2025-02-19T03:17:09Z |
| format | Conference Paper |
| id | ntu-10356/147803 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:17:09Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1478032021-04-20T02:39:06Z Manifold regularized stochastic block model He, Tiantian Bai, Lu Ong, Yew-Soon School of Computer Science and Engineering 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Data Science and Artificial Intelligence Research Centre Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Pattern Clustering Probability Stochastic block models (SBMs) play essential roles in network analysis, especially in those related to unsupervised learning (clustering). Many SBM-based approaches have been proposed to uncover network clusters, by means of maximizing the block-wise posterior probability that generates edges bridging vertices. However, none of them is capable of inferring the cluster preference for each vertex through simultaneously modeling block-wise edge structure, vertex features, and similarities between pairwise vertices. To fill this void, we propose a novel SBM dubbed manifold regularized stochastic model (MrSBM) to perform the task of unsupervised learning in network data in this paper. Besides modeling edges that are within or connecting blocks, MrSBM also considers modeling vertex features utilizing the probabilities of vertex-cluster preference and feature-cluster contribution. In addition, MrSBM attempts to generate manifold similarity of pairwise vertices utilizing the inferred vertex-cluster preference. As a result, the inference of cluster preference may well capture the comparability in the manifold. We design a novel process for network data generation, based on which, we specify the model structure and formulate the network clustering problem using a novel likelihood function. To guarantee MrSBM learns the optimal cluster preference for each vertex, we derive an effective Expectation-Maximization based algorithm for model fitting. MrSBM has been tested on five sets of real-world network data and has been compared with both classical and state-of-the-art approaches to network clustering. The competitive experimental results validate the effectiveness of MrSBM. AI Singapore Accepted version This research is supported by the Data Science and Artificial Intelligence Center at Nanyang Technological University, the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2018-004), and the National Natural Science Foundation of China under Grant 61802317. 2021-04-20T02:39:05Z 2021-04-20T02:39:05Z 2019 Conference Paper He, T., Bai, L. & Ong, Y. (2019). Manifold regularized stochastic block model. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 800-807. https://dx.doi.org/10.1109/ICTAI.2019.00115 9781728137988 https://hdl.handle.net/10356/147803 10.1109/ICTAI.2019.00115 2-s2.0-85081080622 800 807 en AISG-RP-2018-004 © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICTAI.2019.00115 application/pdf |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Pattern Clustering Probability He, Tiantian Bai, Lu Ong, Yew-Soon Manifold regularized stochastic block model |
| title | Manifold regularized stochastic block model |
| title_full | Manifold regularized stochastic block model |
| title_fullStr | Manifold regularized stochastic block model |
| title_full_unstemmed | Manifold regularized stochastic block model |
| title_short | Manifold regularized stochastic block model |
| title_sort | manifold regularized stochastic block model |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Pattern Clustering Probability |
| url | https://hdl.handle.net/10356/147803 |
| work_keys_str_mv | AT hetiantian manifoldregularizedstochasticblockmodel AT bailu manifoldregularizedstochasticblockmodel AT ongyewsoon manifoldregularizedstochasticblockmodel |