About this Research Topic
Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. For example, friend recommendation in social networks can be regarded as a link prediction task, and predicting properties of chemical compounds can be treated as a graph classification task. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today’s world we are generating and gathering data in a much faster and diverse way, real-world graphs are becoming increasingly large-scale and complex. Modern graphs typically contain multiple relations and are also constantly evolving.
In this research topic, we invite submissions of manuscripts that focus on recent advances in research and development of machine learning on complex graphs. Papers can be from any of the following areas related to complex graphs, including but not limited to:
• Graph Kernels
• Graph Summarization
• Graph Coarsening
• Graph Alignment
• Graph Neural Networks for Complex Graphs
• Network Embedding for Complex Graphs
• Machine Learning for Graph Combinatorial Optimization
• Applications of Machine Learning on Complex Graphs
We especially invite submissions for dedicated efforts for developing more advanced techniques that are able to efficiently tackle complex graphs such as dynamic graphs, hypergraphs, heterogeneous graphs, and knowledge graphs.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.