7 papers
A novel method based on node correlation to evaluate the important nodes in complex networks
Pengli Lu, Chen Dong, Yuhong Guo
Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability. Several centrality measures have been proposed recentl…
Distance matrix correlation spectrum of graphs
Pengli Lu, Wenzhi Liu
Let be a simple, connected graph, be the distance matrix of , and be the diagonal matrix of vertex transmissions of . The distance Laplacian matr…
Extremality of graph entropy based on Laplacian degrees of k-uniform hypergraphs
Pengli Lu, Yulong Xue
The graph entropy describes the structural information of graph. Motivated by the definition of graph entropy in general graphs, the graph entropy of hypergraphs based on Laplacian…
A mixed clustering coefficient centrality for identifying essential proteins
Pengli Lu, JingJuan Yu
Essential protein plays a crucial role in the process of cell life. The identification of essential proteins can not only promote the development of drug target technology, but als…
EMH: Extended Mixing H-index centrality for identification important users in social networks based on neighborhood diversity
Pengli Lu, Chen Dong
The rapid expansion of social network provides a suitable platform for users to deliver messages. Through the social network, we can harvest resources and share messages in a very…
Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure
Pengli Lu, Chen Dong
The safety and robustness of the network have attracted the attention of people from all walks of life, and the damage of several key nodes will lead to extremely serious consequen…