Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: K-core decomposition is a popular method that segments a network revealing the underlying hierarchy.  explore the propensity of this decomposition method for structural discrimination among networks by extracting features from each level of the hierarchy.  propose a novel algorithm for Network Comparison using k-coreDecomposition NCKD. The method is effective, efficient and scalable, with computational complexity of $$O|{\mathcal E}|$$, where $${\mathcal E}$$ is the set of edges in the network. The low computational complexity of the method makes it attractive for scalable network comparison.#R##N##R##N#NCKD algorithm decomposes networks and extracts features from the resulting shells. Jensen-Shannon distance beten extracted features quantifies structural differences beten networks.  establish that probability distributions of coreness and intra/inter-shell edges are capable of characterizing different genres of networks and capturing finer structural differences beten networks of the same genre.  experiment with synthetic and real-life networks upi¾?to eight million edges on a single PC. Comparison with two recent state-of-the-art network comparison methods affirms that NCKD outperforms in terms of effectiveness and scalability.
['Rakhi Saxena', 'Sharanjit Kaur', 'Debasis Dash', 'Vasudha Bhatnagar']
I am writing a research paper on a idea Leveraging Structural Hierarchy for Scalable Network Comparison
[{"from": "human", "value": "I am writing a research paper on a idea Leveraging Structural Hierarchy for Scalable Network Comparison"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: K-core decomposition is a popular method that segments a network revealing the underlying hierarchy.  explore the propensity of this decomposition method for structural discrimination among networks by extracting features from each level of the hierarchy.  propose a novel algorithm for Network Comparison using k-coreDecomposition NCKD. The method is effective, efficient and scalable, with computational complexity of $$O|{\\mathcal E}|$$, where $${\\mathcal E}$$ is the set of edges in the network. The low computational complexity of the method makes it attractive for scalable network comparison.#R##N##R##N#NCKD algorithm decomposes networks and extracts features from the resulting shells. Jensen-Shannon distance beten extracted features quantifies structural differences beten networks.  establish that probability distributions of coreness and intra/inter-shell edges are capable of characterizing different genres of networks and capturing finer structural differences beten networks of the same genre.  experiment with synthetic and real-life networks upi\u00be?to eight million edges on a single PC. Comparison with two recent state-of-the-art network comparison methods affirms that NCKD outperforms in terms of effectiveness and scalability."}]