Interaction and collective intelligence on the internet
TAMC'12 Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation
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Modeling topological properties in the field of complex networks in recent years has been rather spectacular and amazing. A key question for characterizing topological structure is how to understand the importance of a node in network? Here we propose a topological potential model to obtain the global ranking which can reflect the importance of each node in given network. Through defining and calculating the topological potential score of each node, we modeled the node importance with activity and local effects. We find that the global ranking is a weak partial order ranked by a single measurement, the strict partial order of all nodes achieved by multi-measurements may be more reasonable. Furthermore we introduce equivalent class schema as a novel node importance evaluation strategy to obtain the ranking results under various measurements. Our experimental results demonstrated that the partial order ranking, as the form of top N is more essential to some real networks, compared with the results by the PageRank algorithms.