Artificial Intelligence for Complex Network: Potential, Methodology and Application

For example, cells are described as complex networks of chemicals linked by chemical reactions [7]; ecological networks link populations together through food chains [64]; and the World Wide Web is a vast virtual network of web pages and hyperlinks [47]. These complex networks are just a few of many examples. The local microscopic behavior of these complex networks often shows disorder. However, at the macroscopic scale, they show simple and even symmetrical structures. In order to understand the transition and evolution of complex systems from microscopic disorder to macroscopic order, current complex network studies mainly fall into the following paradigm: the combination of graph theory and statistical mechanics [3]. They construct the core principle of complex network science, that is, simple random rules and network dynamics together drive the emergence of non-trivial topological structures. Early works mainly focused on the topology of the interactions between the components, i.e., the birth-death process of edges on the graph. The two representative works, the Watts-Strogatz (WS) model and the scale-free model [11, 252], embody this principle and successfully generate graphs that approach real-world complex networks with high clustering coefficients and small average paths or power-law degree distribution. Despite their success in certain domains [17, 221, 222, 235], they do not provide a way to model the dynamics of the nodes, i.e., the change in the node's features.