Web information systems engineering -- WISE 2020 [electronic resource] : 21st International Conference, Amsterdam, The Netherlands, October 20-24, 2020, Proceedings. Part I / Zhisheng Huang, Wouter Beek, Hua Wang, Rui Zhou, Yanchun Zhang (eds.)
This book constitutes the proceedings of the 21st International Conference on Web Information Systems Engineering, WISE 2020, held in Amsterdam, The Netherlands, in October 2020. The 81 full papers presented were carefully reviewed and selected from 190 submissions. The papers are organized in the f...
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Full Text (via Springer) |
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| Other Authors: | , , , , |
| Other title: | WISE 2020. |
| Format: | Electronic Conference Proceeding eBook |
| Language: | English |
| Published: |
Cham :
Springer,
2020.
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| Series: | Lecture notes in computer science ;
12342. LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI. |
| Subjects: |
Table of Contents:
- Intro
- Preface
- Organization
- Contents
- Part I
- Contents
- Part II
- Network Embedding
- Higher-Order Graph Convolutional Embedding for Temporal Networks
- 1 Introduction
- 2 Related Work
- 3 Problem Formulation
- 4 Our Method
- 4.1 Spatial-Temporal Feature Extraction
- 4.2 ST-HNs
- 5 Experiments
- 5.1 Datasets and Baseline Models
- 5.2 Experimental Results
- 5.3 Parameter Sensitivity Analysis
- 6 Conclusion
- References
- RolNE: Improving the Quality of Network Embedding with Structural Role Proximity
- 1 Introduction
- 2 Related Work
- 3 RolNE.
- 4 Experimental Estimate
- 4.1 Barbell Graph
- 4.2 Mirror Karate Club
- 4.3 Air Traffic Network
- 4.4 Enron Email Network
- 5 Conclusion
- References
- Weighted Meta-Path Embedding Learning for Heterogeneous Information Networks
- 1 Introduction
- 2 Related Work
- 2.1 Meta-Path of HIN
- 2.2 Network Embedding
- 3 Preliminaries
- 4 Framework of Proposed WMPE
- 4.1 Approximate Commute Embedding
- 4.2 Meta-Path Generation and Weight Learning
- 4.3 Complexity Analysis
- 5 Experiments
- 5.1 Datasets
- 5.2 Baselines
- 5.3 Meta-Path Filtration
- 5.4 Results of Classification.
- 5.5 Impact of Different Meta-Paths
- 5.6 Analysis of Network Embedding Time
- 5.7 Parameter kr
- 6 Conclusion
- References
- A Graph Embedding Based Real-Time Social Event Matching Model for EBSNs Recommendation
- 1 Introduction
- 2 Related Work
- 2.1 Recommendation Algorithms for EBSNs
- 2.2 Event Planning
- 3 Graph Embedding Based Real-Time Social Event Matching Model
- 3.1 Heterogeneous Information Network of EBSNs
- 3.2 Feature Vector Representation Method
- 3.3 Real-Time Social Event Matching
- 4 Experiments and Evaluation
- 4.1 Dataset Description
- 4.2 Evaluation Criteria.
- 4.3 Performance Comparisons
- 4.4 Discussion on Graph Embedding
- 5 Conclusion and Future Work
- References
- Competitor Mining from Web Encyclopedia: A Graph Embedding Approach
- 1 Introduction
- 2 Related Work
- 3 Framework for Competitor Mining from Web Encyclopedia
- 4 Graph Embedding
- 4.1 Random Walk in the Company Heterogeneous Graph
- 4.2 Graph-Node Embedding Learning
- 4.3 Textual Relevance
- 5 Performance Evaluation
- 5.1 Settings
- 5.2 Results
- 6 Conclusions
- References
- Graph Neural Network
- Fine-Grained Semantics-Aware Heterogeneous Graph Neural Networks.
- 1 Introduction
- 2 Related Work
- 3 Preliminaries
- 4 Proposed Model
- 4.1 Meta-path Level Semantics-Aware Network
- 4.2 Fine-Grained Semantics-Aware Network
- 4.3 Model Training
- 5 Experiments
- 5.1 Datasets and Baselines
- 5.2 Experimental Setup
- 5.3 Node Classification Results
- 6 Conclusion
- References
- DynGCN: A Dynamic Graph Convolutional Network Based on Spatial-Temporal Modeling
- 1 Introduction
- 2 Related Work
- 2.1 Static Graph Representation Learning
- 2.2 Dynamic Graph Representation Learning
- 3 Method
- 3.1 Problem Definition
- 3.2 Architecture Overview.