Name
Abstract | ||
|---|---|---|
Identifying the influential nodes is one of the research focuses in network information mining. Many centrality measures used to evaluate influence abilities of nodes can’t balance between high accuracy and low time complexity. The NL centrality based on the neighbors and importance of edges is proposed which considers the second-degree neighbor’s impact on the influence of a node and utilizes the connectivity and unsubstitutability of edge to distinguish topological position of a node. In order to evaluate the accuracy of NL centrality, the SIR model is used to simulate the process of virus propagation in four real-world networks. Experiment results of monotonicity, validity and efficiency demonstrate that the NL centrality has a competitive performance in distinguishing the influence of nodes and it is suitable for large-scale networks because of the high efficiency in computation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s12083-018-0681-x | Peer-to-peer Networking and Applications |
Keywords | Field | DocType |
Node ranking, Centrality measure, Second-degree neighbor, Importance of edges | Monotonic function,Computer science,Centrality,Theoretical computer science,Information mining,Complex network,Time complexity,Distributed computing,Computation | Journal |
Volume | Issue | ISSN |
12.0 | SP6.0 | 1936-6450 |
Citations | PageRank | References |
1 | 0.38 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zengzhen Shao | 1 | 5 | 3.28 |
| Shulei Liu | 2 | 1 | 0.72 |
| Yanyu Zhao | 3 | 1 | 0.72 |
| Yanxiu Liu | 4 | 1 | 0.72 |