An Improved Algorithm based on Time Domain Network Evolution

Community evolution is the highlight in the field of complex network. The current typical tracking community algorithms largely focus on adopting the traditional similarity functional measurements to capture the similarity between communities at temporal snapshots. However, it doesn’t take into account the actions accumulated with the events and the effects of community members in evolutionary networks. Meanwhile, different communities use traditional tracking methods with a simple similarity function, and as a result, many analogous communities cannot be effectively extracted in the network. To address these shortcomings, in this paper, we propose a much more powerful similarity function to catch and evaluate communities or groups in a successive time frame. We implement a community tracking method in our new function on the basis of previous research, in which we improve accuracy in network structure by taking the diversity corresponding to the active node in network-evolution into consideration. Finally, we find an interesting phenomenon and give a new method to weigh out the relationships involving active nodes within community evolution over time frames. Eventually, the performance of our algorithm is measured by applying it to real datasets and it is tested on tracking community structure and assessing the experimental results that inhibit active nodes extracted from the community. The experimental results show that our algorithm can effectively keep track of community structure and outperform other algorithms.