A Dynamic Variational Framework for Open-World Node Classification in Structured Sequences Article Swipe
Qin Zhang
,
Qincai Li
,
Xiaojun Chen
,
Peng Zhang
,
Shirui Pan
,
Philippe Fournier‐Viger
,
Joshua Zhexue Huang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/icdm54844.2022.00081
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/icdm54844.2022.00081
Structured sequences are a popular data representation, used to model complex data such as traffic networks. A key machine learning task for structured sequences is node classification, that is predicting the class labels of unlabeled nodes. Though many node classification models were proposed, they assume a closed world setting, that all class labels appear in the training data. But in the real-world, the presence of never-before-seen class labels in testing data can considerably degrade a classifier's accuracy. A promising solution to this issue is to build classifiers for an open-world setting, where samples with unknown class labels are continuously observed such that training and testing data may have different class label spaces. Several approaches have been proposed for open-world learning problems in computer vision and natural language processing, but they cannot be applied directly to structured sequences due to the complexity of their non-Euclidean properties and their dynamic nature. This paper addresses this important research gap by proposing a novel Open-world Structured Sequence node Classification (OSSC) model, to learn from structured sequences in an open-world setting. OSSC captures the structural and temporal information via a GCN-based dynamic variational framework. A latent distribution sequence is learned for each node using both stochastic states and deterministic states, to capture the evolution of node attributes and topology, followed by a sampling process to generate node representations. An open-world classification loss is further adopted to ensure that node representations are sensitive to unknown classes. And a combination of Openmax and Softmax is utilized to recognize nodes from unknown classes and to classify others to one of the known classes. Experiments on real-world datasets show that the proposed OSSC method is capable of learning accurate open-world node classifiers from structured sequence data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icdm54844.2022.00081
- OA Status
- green
- Cited By
- 4
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4318823333
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4318823333Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icdm54844.2022.00081Digital Object Identifier
- Title
-
A Dynamic Variational Framework for Open-World Node Classification in Structured SequencesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-01Full publication date if available
- Authors
-
Qin Zhang, Qincai Li, Xiaojun Chen, Peng Zhang, Shirui Pan, Philippe Fournier‐Viger, Joshua Zhexue HuangList of authors in order
- Landing page
-
https://doi.org/10.1109/icdm54844.2022.00081Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/10072/421806Direct OA link when available
- Concepts
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Computer science, Classifier (UML), Node (physics), Artificial intelligence, Class (philosophy), Machine learning, Sequence labeling, Data mining, Theoretical computer science, Task (project management), Management, Economics, Structural engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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