Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Community structure is a fundamental feature for many networks. The problem of discovering communities in those networks thus has been attracting a lot of research. Hover, due to the rapid increase of networks’ scale and the availability of real communities in many networks, the task of detecting communities in large real networks remains a challenging problem. ,  study the structure of various large real networks and their ground-truth community structures and observe an interesting phenomenon: the difference of degrees (abbreviated as dod) of connected nodes follows a heavy-tailed distribution with an approximate por-law tail for large dod, in both original network and community structure but to different extents. With the aim to explore the effect of this observation on identifying communities in real large networks,  propose a ighting strategy and further embed it into two prominent community detection algorithms. Comparisons against the state of the arts demonstrate a very promising performance of the proposed ighting strategy.
['Yanmei Hu', 'Bo Yang']
I am writing a research paper on a idea Characterizing the structure of large real networks to improve community detection
[{"from": "human", "value": "I am writing a research paper on a idea Characterizing the structure of large real networks to improve community detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Community structure is a fundamental feature for many networks. The problem of discovering communities in those networks thus has been attracting a lot of research. Hover, due to the rapid increase of networks\u2019 scale and the availability of real communities in many networks, the task of detecting communities in large real networks remains a challenging problem. ,  study the structure of various large real networks and their ground-truth community structures and observe an interesting phenomenon: the difference of degrees (abbreviated as dod) of connected nodes follows a heavy-tailed distribution with an approximate por-law tail for large dod, in both original network and community structure but to different extents. With the aim to explore the effect of this observation on identifying communities in real large networks,  propose a ighting strategy and further embed it into two prominent community detection algorithms. Comparisons against the state of the arts demonstrate a very promising performance of the proposed ighting strategy."}]