Name
Abstract | ||
|---|---|---|
Identifying the influential spreaders is an important issue in understanding and controlling the spreading processes in social networks. The key to the influential spreader identification problem is how to evaluate the spreading ability of the nodes. Centralities such as degree, semi-local centrality, betweenness, closeness, k-shell are usually used as evaluation metric. However, we observe that these centralities are sensitive not only to the spreading probability, but also to the network structure. We are not sure which centrality is efficient when we face a new network. In this paper, we propose a robust method named Adjustable multi-Hops Spreading (AHS). In AHS, we refine the influence of a node into direct influence and indirect influence, and then integrate them with a adjustable parameter. The experimental results in both real social networks and artificial networks show that AHS outperforms other centralities in effectiveness, robustness and the distinguish ability. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00500-017-2847-5 | Soft Comput. |
Keywords | Field | DocType |
Social networks, Influential nodes, Spreading, Centrality measure | Data mining,Social network,Closeness,Computer science,Centrality,Robustness (computer science),Betweenness centrality,Artificial intelligence,Parameter identification problem,Machine learning,Network structure | Journal |
Volume | Issue | ISSN |
23 | 4 | 1433-7479 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||