Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years, the evolution of online social networks has become an important research topic in online social network analysis. An important approach to this problem is to detect community evolution events so as to understand the evolution of the whole network. Considering the huge amount of data in large social networks, an efficient and scalable community evolution detection algorithm is necessary. ,  focus on community evolution events detection in dynamic social networks. First,  divide the Facebook and DBLP data set into a series of snapshots and apply the Louvain algorithm to find the communities in each snapshot. Then,  propose a light ight evolution events detection algorithm to find the community evolution patterns beten adjacent snapshots, which statistically show the evolution trend of the entire network. Simulation results show that our algorithm can effectively detect the community evolution events in online social networks.
['Yang Sun', 'Junhua Tang', 'Li Pan', 'Jianhua Li']
I am writing a research paper on a idea Matrix Based Community Evolution Events Detection in Online Social Networks
[{"from": "human", "value": "I am writing a research paper on a idea Matrix Based Community Evolution Events Detection in Online Social Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years, the evolution of online social networks has become an important research topic in online social network analysis. An important approach to this problem is to detect community evolution events so as to understand the evolution of the whole network. Considering the huge amount of data in large social networks, an efficient and scalable community evolution detection algorithm is necessary. ,  focus on community evolution events detection in dynamic social networks. First,  divide the Facebook and DBLP data set into a series of snapshots and apply the Louvain algorithm to find the communities in each snapshot. Then,  propose a light ight evolution events detection algorithm to find the community evolution patterns beten adjacent snapshots, which statistically show the evolution trend of the entire network. Simulation results show that our algorithm can effectively detect the community evolution events in online social networks."}]