Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study a new notion of graph centrality based on absorbing random walks. Given a graph G=(V, E) and a set of query nodes Q &#x0xCD; V,  aim to identify the k most central nodes in G with respect to Q. Specifically,  consider central nodes to be absorbing for random walks that start at the query nodes~Q. The goal is to find the set of k central nodes that minimizes the expected length of a random walk until absorption. The proposed measure, which  call k absorbing random-walk centrality, favors diverse sets, as it is beneficial to place the k absorbing nodes in different parts of the graph so as to intercept random walks that start from different query nodes. Although similar problem definitions have been considered in the literature, e.g.,in information-retrieval settings where the goal is to diversify b-search results, in this paper  study the problem formally and prove some of its properties.  find that the problem is NP-hard, while the objective function ismonotone and supermodular, implying that a greedy algorithmprovides solutions with an approximation guarantee. On the other hand, the greedy algorithm involves expensivematrix operations that make it prohibitive to employ on large datasets. To confront this challenge,  explore the performance of efficient heuristics.
['Charalampos Mavroforakis', 'Michael Mathioudakis', 'Aristides Gionis']
I am writing a research paper on a idea Absorbing Random-Walk Centrality: Theory and Algorithms
[{"from": "human", "value": "I am writing a research paper on a idea Absorbing Random-Walk Centrality: Theory and Algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study a new notion of graph centrality based on absorbing random walks. Given a graph G=(V, E) and a set of query nodes Q &#x0xCD; V,  aim to identify the k most central nodes in G with respect to Q. Specifically,  consider central nodes to be absorbing for random walks that start at the query nodes~Q. The goal is to find the set of k central nodes that minimizes the expected length of a random walk until absorption. The proposed measure, which  call k absorbing random-walk centrality, favors diverse sets, as it is beneficial to place the k absorbing nodes in different parts of the graph so as to intercept random walks that start from different query nodes. Although similar problem definitions have been considered in the literature, e.g.,in information-retrieval settings where the goal is to diversify b-search results, in this paper  study the problem formally and prove some of its properties.  find that the problem is NP-hard, while the objective function ismonotone and supermodular, implying that a greedy algorithmprovides solutions with an approximation guarantee. On the other hand, the greedy algorithm involves expensivematrix operations that make it prohibitive to employ on large datasets. To confront this challenge,  explore the performance of efficient heuristics."}]