SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed Networks Article Swipe
Yang Li
,
Bo Yang
,
Xuehua Zhao
,
Zhejian Yang
,
Hechang Chen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.10955
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.10955
Signed network structure discovery has received extensive attention and has become a research focus in the field of network science. However, most of the existing studies are focused on the networks with a single structure, e.g., community or bipartite, while ignoring multiple structures, e.g., the coexistence of community and bipartite structures. Furthermore, existing studies were faced with challenge regarding large-scale signed networks due to their high time complexity, especially when determining the number of clusters in the observed network without any prior knowledge. In view of this, we propose a mathematically principled method for signed network multiple structure discovery named the Signed Stochastic Block Model (SSBM). The SSBM can capture the multiple structures contained in signed networks, e.g., community, bipartite, and coexistence of them, by adopting a probabilistic model. Moreover, by integrating the minimum message length (MML) criterion and component-wise EM (CEM) algorithm, a scalable learning algorithm that has the ability of model selection is proposed to handle large-scale signed networks. By comparing state-of-the-art methods on synthetic and real-world signed networks, extensive experimental results demonstrate the effectiveness and efficiency of SSBM in discovering large-scale exploratory signed networks with multiple structures.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2304.10955
- https://arxiv.org/pdf/2304.10955
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366835612
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4366835612Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2304.10955Digital Object Identifier
- Title
-
SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-21Full publication date if available
- Authors
-
Yang Li, Bo Yang, Xuehua Zhao, Zhejian Yang, Hechang ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2304.10955Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2304.10955Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2304.10955Direct OA link when available
- Concepts
-
Bipartite graph, Computer science, Stochastic block model, Signed graph, Probabilistic logic, Complex network, Scalability, Focus (optics), Scale (ratio), Theoretical computer science, Artificial intelligence, Geography, Cartography, Graph, Database, Cluster analysis, Physics, World Wide Web, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.attention | 7 |
| abstract_inverted_index.bipartite | 49 |
| abstract_inverted_index.challenge | 57 |
| abstract_inverted_index.community | 36, 47 |
| abstract_inverted_index.comparing | 162 |
| abstract_inverted_index.contained | 113 |
| abstract_inverted_index.criterion | 137 |
| abstract_inverted_index.discovery | 3, 98 |
| abstract_inverted_index.extensive | 6, 171 |
| abstract_inverted_index.networks, | 116, 170 |
| abstract_inverted_index.networks. | 160 |
| abstract_inverted_index.regarding | 58 |
| abstract_inverted_index.selection | 153 |
| abstract_inverted_index.structure | 2, 97 |
| abstract_inverted_index.synthetic | 166 |
| abstract_inverted_index.Stochastic | 102 |
| abstract_inverted_index.algorithm, | 142 |
| abstract_inverted_index.bipartite, | 38, 119 |
| abstract_inverted_index.community, | 118 |
| abstract_inverted_index.efficiency | 178 |
| abstract_inverted_index.especially | 68 |
| abstract_inverted_index.knowledge. | 82 |
| abstract_inverted_index.principled | 91 |
| abstract_inverted_index.real-world | 168 |
| abstract_inverted_index.structure, | 34 |
| abstract_inverted_index.structures | 112 |
| abstract_inverted_index.coexistence | 45, 121 |
| abstract_inverted_index.complexity, | 67 |
| abstract_inverted_index.demonstrate | 174 |
| abstract_inverted_index.determining | 70 |
| abstract_inverted_index.discovering | 182 |
| abstract_inverted_index.exploratory | 184 |
| abstract_inverted_index.integrating | 131 |
| abstract_inverted_index.large-scale | 59, 158, 183 |
| abstract_inverted_index.structures, | 42 |
| abstract_inverted_index.structures. | 50, 189 |
| abstract_inverted_index.Furthermore, | 51 |
| abstract_inverted_index.experimental | 172 |
| abstract_inverted_index.effectiveness | 176 |
| abstract_inverted_index.probabilistic | 127 |
| abstract_inverted_index.component-wise | 139 |
| abstract_inverted_index.mathematically | 90 |
| abstract_inverted_index.state-of-the-art | 163 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |