SSDBA:the stretch shrink distance based algorithm for link prediction in social networks

In the field of social network analysis,Link Predic-tion is one of the hottest topics which has been attracted attentions in academia and industry.So far,literatures for solving link prediction can be roughly divided into two categories:similarity-based and learning-based methods.The learning-based methods have higher accuracy,but their time complexities are too high for complex networks.However,the similarity-based methods have the advantage of low time consumption,so improving their accuracy becomes a key issue.In this paper,we employ community structures of social networks to improve the prediction accuracy and propose the stretch shrink distance based algorithm(SSDBA),In SSDBA,we first detect communities of a social network and identify active nodes based on community average threshold(CAT)and node average threshold(NAT)in each community.Second,we propose the stretch shrink distance(SSD)model to iteratively calculate the changes of distances between active nodes and their local neighbors.Finally,we make predictions when these links'distances tend to converge.Furthermore,extensive parameters learning have been carried out in experiments.We compare our SSDBA with other popular approaches.Experimental results validate the effectiveness and efficiency of proposed algorithm.

Decoding human brain functions: Multi-modal, multi-scale insights

Unraveling the intricate relationship between the structure and function of the human brain remains a central and unresolved question in neuroscience.Ethical considerations impose significant constraints on invasive techniques in human neuroscience research.Consequently,knowledge about human brain function often relies on animal models to provide valuable discoveries and insights.However,caution is warranted,as findings from animal studies may not always be directly translatable to humans,especially when investigating higher cognitive functions.