Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Predicting missing links in complex networks has a theoretical interest and practical significance in social network analysis. ,  study the link prediction results as the change of the exponent on common neighbor's degree and find some regular pattern beten different networks and different exponent. Then  present a local network metric based on the regular pattern to estimate the likelihood of the existence of a link beten two nodes. Our new metric takes the exponent of common neighbor's degree into consideration for link prediction. The paper also recommends the value range of the exponent.  compare nine ll-known local information metrics and the new metric on eight real networks. The result evaluated by AUC indicates that our new metric, namely Degree Exponent Change metric, have a better prediction accuracy than other ll-known metrics.
['Yuxiao Dong', 'Qing Ke', 'Jun Rao', 'Bin Wu']
I am writing a research paper on a idea Predicting missing links via local feature of common neighbors
[{"from": "human", "value": "I am writing a research paper on a idea Predicting missing links via local feature of common neighbors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Predicting missing links in complex networks has a theoretical interest and practical significance in social network analysis. ,  study the link prediction results as the change of the exponent on common neighbor's degree and find some regular pattern beten different networks and different exponent. Then  present a local network metric based on the regular pattern to estimate the likelihood of the existence of a link beten two nodes. Our new metric takes the exponent of common neighbor's degree into consideration for link prediction. The paper also recommends the value range of the exponent.  compare nine ll-known local information metrics and the new metric on eight real networks. The result evaluated by AUC indicates that our new metric, namely Degree Exponent Change metric, have a better prediction accuracy than other ll-known metrics."}]