The Fifth International Workshop on Deep Learning for Graphs
DL4G@WWW 2020
- URL: https://www.aminer.cn/dl4g_www2020
- Event Date: 2020-04-21 ~ 2020-04-22
- Submission Date: 2020-01-20
- Location: Taipei, Taiwan
The 2019 edition of International Workshop of Deep Learning for Graphs (DL4G) was the most registered one among all 20+ workshops at WWW 2019! We hope to welcome all of you to its 2020 edition at the Web Conference at Taipei!
The fifth International Workshop of Deep Learning for Graphs (DL4G@WWW2020) is a full day workshop to be held on April 21, 2020 at Taipei during the Web Conference. It aims to provide a forum for presenting the most recent advances in embedding and representation learning for structured data as well as deep learning for graphs to unearth rich knowledge. We expect novel research works that address various aspects and challenges around this topic, including learning representation for large-scale and dynamic networks, heterogeneous network embedding, scalable and efficient algorithms for other structured data embedding, deep learning methodologies for graph-structured data, novel platforms and applications supporting structured data embeddings, and beyond. We hope this dedicated workshop will foster further research discussions and development in this field. The DL4G workshop invites leading experts in the area from all over the world. It will become a networking event both for connecting researchers from diverse research areas such as algorithms, data mining, and machine learning, and for connecting researchers from different geographic regions.
The program of the DL4G workshop will focus the attention on presenting and discussing the state-of-the-art, open problems, challenges and latest models, techniques and algorithms in the field of deep learning for graphs and structured data embedding. In this context, topics of interest include but are not limited to:
• Graph neural networks;
• Network representation learning theories and foundations;
• Representation learning for spatio-temporal data;
• Representation learning for tree-structured data;
• Representation learning for big networks, heterogeneous networks and/or dynamic networks;
• Representation learning across multiple networks;
• Representation learning for graphs with text;
• Knowledge base embedding;
• Deep learning for graphs;
• Graph theories and network embeddings;
• Visualization for network embeddings;
• Efficient graph embedding algorithms;
• Scalable graph embedding models and frameworks;
• Novel network embedding applications;
• Representation learning and traditional structural mining.
• Graph kernels and similarity
• Graph based deep learning for text and NLP applications
• Deep learning for influence maximization in social networks
• Graph autoencoders for community detection
• Graph/node embeddings for fraud detection
Important Dates
§ Submission Deadline: Jan 20, 2020
§ Notification Date: Feb. 3, 2020.
§ Camera-Ready Submission: Feb. 17, 2020
§ Workshop Date: April 21, 2020
Submission Guidelines
The DL4G 2020 workshop encourages submissions that present both original results and preliminary/existing work on structured data embedding and deep learning for graphs. We explicitly welcome extended-abstract submissions to introduce preliminary and arXiv work on related topics, as well as recently-published research at top conferences. The extended abstracts can option to be not archived in the WWW Companion proceeding. Therefore, this workshop accepts both full papers (4 to 8 pages) for original results and extended abstracts (1 to 2 pages) for published or ongoing work. All submissions must conform to the WWW 2020 main conference submission format: the ACM SIG Proceedings template with a font size no smaller than 9pt.
Submission site at easychair: https://easychair.org/conferences/?conf=dl4g
The fifth International Workshop of Deep Learning for Graphs (DL4G@WWW2020) is a full day workshop to be held on April 21, 2020 at Taipei during the Web Conference. It aims to provide a forum for presenting the most recent advances in embedding and representation learning for structured data as well as deep learning for graphs to unearth rich knowledge. We expect novel research works that address various aspects and challenges around this topic, including learning representation for large-scale and dynamic networks, heterogeneous network embedding, scalable and efficient algorithms for other structured data embedding, deep learning methodologies for graph-structured data, novel platforms and applications supporting structured data embeddings, and beyond. We hope this dedicated workshop will foster further research discussions and development in this field. The DL4G workshop invites leading experts in the area from all over the world. It will become a networking event both for connecting researchers from diverse research areas such as algorithms, data mining, and machine learning, and for connecting researchers from different geographic regions.
The program of the DL4G workshop will focus the attention on presenting and discussing the state-of-the-art, open problems, challenges and latest models, techniques and algorithms in the field of deep learning for graphs and structured data embedding. In this context, topics of interest include but are not limited to:
• Graph neural networks;
• Network representation learning theories and foundations;
• Representation learning for spatio-temporal data;
• Representation learning for tree-structured data;
• Representation learning for big networks, heterogeneous networks and/or dynamic networks;
• Representation learning across multiple networks;
• Representation learning for graphs with text;
• Knowledge base embedding;
• Deep learning for graphs;
• Graph theories and network embeddings;
• Visualization for network embeddings;
• Efficient graph embedding algorithms;
• Scalable graph embedding models and frameworks;
• Novel network embedding applications;
• Representation learning and traditional structural mining.
• Graph kernels and similarity
• Graph based deep learning for text and NLP applications
• Deep learning for influence maximization in social networks
• Graph autoencoders for community detection
• Graph/node embeddings for fraud detection
Important Dates
§ Submission Deadline: Jan 20, 2020
§ Notification Date: Feb. 3, 2020.
§ Camera-Ready Submission: Feb. 17, 2020
§ Workshop Date: April 21, 2020
Submission Guidelines
The DL4G 2020 workshop encourages submissions that present both original results and preliminary/existing work on structured data embedding and deep learning for graphs. We explicitly welcome extended-abstract submissions to introduce preliminary and arXiv work on related topics, as well as recently-published research at top conferences. The extended abstracts can option to be not archived in the WWW Companion proceeding. Therefore, this workshop accepts both full papers (4 to 8 pages) for original results and extended abstracts (1 to 2 pages) for published or ongoing work. All submissions must conform to the WWW 2020 main conference submission format: the ACM SIG Proceedings template with a font size no smaller than 9pt.
Submission site at easychair: https://easychair.org/conferences/?conf=dl4g
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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