Ecological balance is one of the most attractive topics in biological, environmental, earth sciences, and so on. However, due to the complexity of ecosystems, it is not easy to find a perfect way to conclusively explain all the potential impacts. In this paper, by considering several important elements, we seek to build a dynamic network model to predict the Earth’s health, trying to identify and explain how the human behavior and policies affect the model results. We firstly empirically analyze both the topological properties and time-dependent features of nodes and propose an Earth’s health index based on Shannon Entropy. Secondly, we identify the importance of each element by a machine learning approach. Thirdly, we use a spreading model to predict the Earth’s health. Finally, we integrate the topological property and the proposed health index to identify the influential nodes in the observed ecological network. Experimental results show that the oceans are the key nodes in affecting the Earth’s health, and Big countries are also important nodes in influencing the Earth’s health. In addition, the results suggest a possible solution that returning more living lands might be an effective way to solve the dilemma of ecological balance. 1. Introduction Ecological balance is one of the most attractive topics in biological, environmental, Earth sciences and many other related disciplines [1], especially since the industrialization has been undergoing for about two hundred years. To better understand how biosphere responds to the increasing pressure (e.g., population explosion, water and air pollution, climate change), there is a vast class of researches devoted to discovering possible solutions in alleviating pains of the Earth. However, due to the complexity of ecosystems, it is not easy to find a perfect way to conclusively explain all the potential impacts [2] that is responsible for the ecological fragility [3, 4]. Among various studies, Ecological Network Analysis (ENA) [5, 6] is regarded as one promising methodology to assess the Earth’s health [7]. Ecological networks can be extracted from various information, resulting in different kinds of networks, where each node represents a nation, a continent, an ocean, a habitat, or a park, and an edge is present when two nodes are directed or mutually influenced, varying from economic impact, population flow to environmental pollution, economic level, and so forth [5, 8]. Since the network information is not explicitly provided, we start our research by constructing the global ecological network via
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