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|a 10.1109/TPAMI.2024.3378729
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|a eng
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| 100 |
1 |
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|a Zhang, Kai
|e verfasserin
|4 aut
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| 245 |
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|a A Transformative Topological Representation for Link Modeling, Prediction and Cross-Domain Network Analysis
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a Date Revised 07.08.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Many complex social, biological, or physical systems are characterized as networks, and recovering the missing links of a network could shed important lights on its structure and dynamics. A good topological representation is crucial to accurate link modeling and prediction, yet how to account for the kaleidoscopic changes in link formation patterns remains a challenge, especially for analysis in cross-domain studies. We propose a new link representation scheme by projecting the local environment of a link into a "dipole plane", where neighboring nodes of the link are positioned via their relative proximity to the two anchors of the link, like a dipole. By doing this, complex and discrete topology arising from link formation is turned to differentiable point-cloud distribution, opening up new possibilities for topological feature-engineering with desired expressiveness, interpretability and generalization. Our approach has comparable or even superior results against state-of-the-art GNNs, meanwhile with a model up to hundreds of times smaller and running much faster. Furthermore, it provides a universal platform to systematically profile, study, and compare link-patterns from miscellaneous real-world networks. This allows building a global link-pattern atlas, based on which we have uncovered interesting common patterns of link formation, i.e., the bridge-style, the radiation-style, and the community-style across a wide collection of networks with highly different nature
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|a Journal Article
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| 700 |
1 |
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|a Shen, Junchen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a He, Gaoqi
|e verfasserin
|4 aut
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| 700 |
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|a Sun, Yu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ling, Haibin
|e verfasserin
|4 aut
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| 700 |
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|a Zha, Hongyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Honglin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Jie
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 46(2024), 9 vom: 01. Aug., Seite 6126-6138
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|x 1939-3539
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|g volume:46
|g year:2024
|g number:9
|g day:01
|g month:08
|g pages:6126-6138
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|u http://dx.doi.org/10.1109/TPAMI.2024.3378729
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