Name
Abstract | ||
|---|---|---|
Communities are ubiquitous in nature and society. Individuals that share common properties often self-organize to form communities. Avoiding the shortages of computation complexity, pre-given information and unstable results in different run, in this paper, we propose a simple and efficient method to deepen our understanding of the emergence and diversity of communities in complex systems. By introducing the rational random selection, our method reveals the hidden deterministic and normal diverse community states of community structure. To demonstrate this method, we test it with real-world systems. The results show that our method could not only detect community structure with high sensitivity and reliability, but also provide instructional information about the hidden deterministic community world and the real normal diverse community world by giving out the core-community, the real-community, the tide and the diversity. Thizs is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in complex systems. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.physa.2018.02.127 | Physica A: Statistical Mechanics and its Applications |
Keywords | Field | DocType |
Similarity,Core-community,Real-community,Diversity | Data science,Complex system,Community structure,Quantum mechanics,Economic shortage,Computation complexity,Mathematics | Journal |
Volume | ISSN | Citations |
501 | 0378-4371 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| YunFeng Chang | 1 | 0 | 0.34 |
| SeungKee Han | 2 | 0 | 0.34 |
| Xidong Wang | 3 | 3 | 2.39 |