Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Visualization is a porful paradigm for exploratory data analysis. Visualizing large graphs, hover, often results in a meaningless hairball. ,  propose a different approach that helps the user adaptively explore large million-node graphs from a local perspective. For nodes that the user investigates,  propose to only show the neighbors with the most subjectively interesting neighborhoods.  contribute novel ideas to measure this interestingness in terms of how surprising a neighborhood is given the background distribution, as ll as how ll it fits the nodes the user chose to explore.  introduce FACETS, a fast and scalable method for visually exploring large graphs. By implementing our above ideas, it allows users to look into the forest through its trees. Empirical evaluation shows that our method works very ll in practice, providing rankings of nodes that match interests of users. Moreover, as it scales linearly, FACETS is suited for the exploration of very large graphs.
['Robert Pienta', 'Zhiyuan Lin', 'Minsuk Kahng', 'Jilles Vreeken', 'Partha P. Talukdar', 'James Abello', 'Ganesh Parameswaran', 'Duen Horng Chau']
I am writing a research paper on a idea Seeing the Forest through the Trees: Adaptive Local Exploration of Large Graphs
[{"from": "human", "value": "I am writing a research paper on a idea Seeing the Forest through the Trees: Adaptive Local Exploration of Large Graphs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Visualization is a porful paradigm for exploratory data analysis. Visualizing large graphs, hover, often results in a meaningless hairball. ,  propose a different approach that helps the user adaptively explore large million-node graphs from a local perspective. For nodes that the user investigates,  propose to only show the neighbors with the most subjectively interesting neighborhoods.  contribute novel ideas to measure this interestingness in terms of how surprising a neighborhood is given the background distribution, as ll as how ll it fits the nodes the user chose to explore.  introduce FACETS, a fast and scalable method for visually exploring large graphs. By implementing our above ideas, it allows users to look into the forest through its trees. Empirical evaluation shows that our method works very ll in practice, providing rankings of nodes that match interests of users. Moreover, as it scales linearly, FACETS is suited for the exploration of very large graphs."}]