Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Graph clustering or community detection constitutes an important task for investigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such as spectral methods, typically suffer from high time and space complexity. In this article,  present CORECLUSTER, an efficient graph clustering framework based on the concept of graph degeneracy, that can be used along with any known graph clustering algorithm. Our approach capitalizes on processing the graph in a hierarchical manner provided by its core expansion sequence, an ordered partition of the graph into different levels according to the k-core decomposition. Such a partition provides a way to process the graph in an incremental manner that preserves its clustering structure, while making the execution of the chosen clustering algorithm much faster due to the smaller size of the graph's partitions onto which the algorithm operates.
['Christos Giatsidis', 'Fragkiskos D. Malliaros', 'Dimitrios M. Thilikos', 'Michalis Vazirgiannis']
I am writing a research paper on a idea CORECLUSTER: a degeneracy based graph clustering framework
[{"from": "human", "value": "I am writing a research paper on a idea CORECLUSTER: a degeneracy based graph clustering framework"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Graph clustering or community detection constitutes an important task for investigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such as spectral methods, typically suffer from high time and space complexity. In this article,  present CORECLUSTER, an efficient graph clustering framework based on the concept of graph degeneracy, that can be used along with any known graph clustering algorithm. Our approach capitalizes on processing the graph in a hierarchical manner provided by its core expansion sequence, an ordered partition of the graph into different levels according to the k-core decomposition. Such a partition provides a way to process the graph in an incremental manner that preserves its clustering structure, while making the execution of the chosen clustering algorithm much faster due to the smaller size of the graph's partitions onto which the algorithm operates."}]