Complexity

Deep Structure Representation and Learning for Complex Information Networks


Publishing date
01 Jun 2021
Status
Closed
Submission deadline
05 Feb 2021

Lead Editor

1Macquarie University, Sydney, Australia

2Chinese Academy of Sciences, Beijing, China

3Harvard University, Boston, USA

This issue is now closed for submissions.
More articles will be published in the near future.

Deep Structure Representation and Learning for Complex Information Networks

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Complex information network analysis is a vital research area in information diffusion, business marketing, personalized recommendation, and social influence analysis, etc. The complex information networks we focus on in this Special Issue refer to dynamic, heterogeneous, attribute, online, and/or direct information networks. In heterogeneous information networks, nodes are linked with multiple different types of edges, and nodes represent different types of entities (e.g., bus stations and metro stations in public transportation networks). In dynamic information networks, the topology of those networks changes over time as nodes/edges are added or removed. In addition, edges are always directed, and nodes are characterized by multiple attributes. How to mine behavioural features and rules from complex information networks is essential to related applications. Pairwise relations can only provide insights about local neighbourhoods and might not infer global hierarchical network structures, which is crucial for complex networks. Therefore, we require refined methods and feature extraction algorithms to enhance the prediction accuracy of complex systems, due to the complexity and diversity of the network data. How to design effective network representations that are capable of preserving hierarchical structures of networks is a promising direction for further work.

Learning a deep structure representation for complex information networks aims to project a graph into a low-dimensional vector space. Data mining and machine learning methods can easily deal with the network representation for further applications, such as link prediction, node classification, anomaly detection, and community detection. Due to the complexity of information networks, designing a novel network representation to deal with the heterogeneity and evolution of networks is a challenging and promising topic.

The aim of this Special Issue is to solicit contributions to fundamental research in deep structure representation and learning for complex information networks. Submissions discussing and introducing new algorithmic foundations and representation formalisms are also welcome. We also seek studies on network representation applied to business, sociology, biology, health, and other industrial applications of complex information networks that help to solve real-world problems. Review articles discussing the current state of the art are also welcomed.

Potential topics include but are not limited to the following:

  • Graph embedding
  • Graph neural networks
  • Generative adversarial net
  • Deep neural networks for social networks
  • Online social network analysis
  • Random walk-based algorithms
  • Matrix factorization for social networks
  • Social influence analysis
  • Link prediction
  • Node classification
  • Social anomaly detection
  • Community detection
  • Social recommender systems
  • Knowledge graph learning
  • Graph generation
  • Network dynamics
  • Heterogeneous social network analysis
  • Subgraph structure learning

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 6659695
  • - Research Article

Prediction of circRNA-miRNA Associations Based on Network Embedding

Wei Lan | Mingrui Zhu | ... | Shirui Pan
  • Special Issue
  • - Volume 2021
  • - Article ID 8859225
  • - Research Article

A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social Networks

Xiaoyan Xu | Wei Lv | ... | Yusen Li
  • Special Issue
  • - Volume 2021
  • - Article ID 5526412
  • - Research Article

Anonymous Authentication and Key Agreement Scheme Combining the Group Key for Vehicular Ad Hoc Networks

Mei Sun | Yuyan Guo | ... | MingMing Jiang
  • Special Issue
  • - Volume 2021
  • - Article ID 6661901
  • - Research Article

Multi-channel Convolutional Neural Network Feature Extraction for Session Based Recommendation

Zhenyan Ji | Mengdan Wu | ... | José Enrique Armendáriz Íñigo
  • Special Issue
  • - Volume 2021
  • - Article ID 8841822
  • - Research Article

Graph-Based Analysis of RNA Secondary Structure Similarity Comparison

Lina Yang | Yang Liu | ... | Jun Wu
  • Special Issue
  • - Volume 2021
  • - Article ID 6644111
  • - Research Article

Multiplex Network Embedding Model with High-Order Node Dependence

Nianwen Ning | Qiuyue Li | ... | Bin Wu
  • Special Issue
  • - Volume 2021
  • - Article ID 8834652
  • - Research Article

No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Peng Xu | Man Guo | ... | Yujun Li
  • Special Issue
  • - Volume 2021
  • - Article ID 6610965
  • - Research Article

A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

Chengyao Lv | Deng Pan | ... | Zong Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 6061939
  • - Research Article

Vehicle Type Recognition Algorithm Based on Improved Network in Network

Erxi Zhu | Min Xu | De Chang Pi
  • Special Issue
  • - Volume 2020
  • - Article ID 8715619
  • - Research Article

Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network

Xiaolong Deng | Hao Ding | ... | Tiejun Lv
Complexity
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
Acceptance rate43%
Submission to final decision63 days
Acceptance to publication35 days
CiteScore3.300
Journal Citation Indicator0.690
Impact Factor2.833
 Submit

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.