动态复杂网络中节点影响力的研究进展

节点影响力的识别和预测具有重要的理论意义和应用价值,是复杂网络的热点研究领域.目前大多数研究方法都是针对静态网络或动态网络某一时刻的快照进行的,然而在实际应用场景中,社会、生物、信息、技术等复杂网络都是动态演化的.因此在动态复杂网络中评估节点影响力以及预测节点未来影响力,特别是在网络结构变化之前的预测更具意义.本文系统地总结了动态复杂网络中节点影响力算法面临的三类挑战,即在增长网络中,节点影响力算法的计算复杂性和时间偏见;网络实时动态演化时,节点影响力算法的适应性;网络结构微扰或突变时,节点影响力算法的鲁棒性,以及利用网络结构演变阐释经济复杂性涌现的问题.最后总结了这一研究方向几个待解决的问题并指出未来可能的发展方向.

Node influence of the dynamic networks

Crucial to the physicists’strong interest in the field is the fact that such macroscopic properties typically arise as the result of a myriad of interactions between the system constituents.Network science aims at simplifying the study of a given complex system by representing it as a network,a collection of nodes and edges interconnecting them.Nowadays,it is widely recognized that some of the structural traits of networks are in fact ubiquitous properties in real systems.The identification and prediction of node influence are of great theoretical and practical significance to be known as a hot research field of complex networks.Most of current research advance is focused on static network or a snapshot of dynamic networks at a certain moment.However,in practical application scenarios,mostly complex networks extracted from society,biology,information,technology are evolving dynamically.Therefore,it is more meaningful to evaluate the node's influence in the dynamic network and predict the future influence of the node,especially before the change of the network structure.In this summary,we contribute on reviewing the improvement of node influence in dynamical networks,which involves three tasks:algorithmic complexity and time bias in growing networks;algorithmic applicability in time varying networks;algorithmic robustness in a dynamical network with small or sharp perturbation.Furthermore,we overview the framework of economic complexity based on dynamical network structure.Lastly,we point out the forefront as well as critical challenges of the field.