Critical Nodes Identification in Complex Networks
Abstract
:1. Introduction
- (1)
- We draw inspiration from the theory of structural holes [33] and only consider the node’s local neighborhood information to evaluate the importance of nodes. This makes the algorithm more computationally attractive for large-scale networks;
- (2)
- The proposed algorithm can effectively identify the hubs with numerous structural holes, which play important role in bridging different clusters of the network;
- (3)
- Empirical analyses on real and synthetic networks demonstrate that the proposed method can outperform Deg, k-shell, ME, CI, DDN [34], and random ranking method (Rand).
2. Materials and Methods
2.1. Measurement of Node Importance Based on Degree and Structural Hole Count
Algorithm 1 the DSHC Method |
Input: Network adjacency matrix A(aij), degree of network nodes k, the size of network N |
Output: The DSHC value of each node |
1: for i = 1 to N |
2: Si = find (A(aij) =1) // Find the neighbors Si of node i |
3: len(Si) = length(Si) // Number of neighbors of node i |
4: for j = 1:len(Si) |
5: Si_j= find (A(ajk) =1) // Find the neighbors Si_j of neighbor j of node i |
6: Δij= |Si_j - intersect(Si, Si_j)| // calculate the number of structural holes formed between node i and j with node i as the intermediary |
7: |
8: end for |
9: DSHCi = sum(DSHCi_j) // according to Equation(1) |
10: end for |
11: Return DSHC value of each node |
2.2. Benchmark Methods
3. How to Evaluate the Performance
4. Data Description
5. Results and Analysis
5.1. Experiments in Real Networks
5.2. Experiments in Synthetic Networks
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Network | N | M | <k> | C | ksmax | L |
---|---|---|---|---|---|---|
USAir | 332 | 2126 | 12.807 | 0.625 | 26 | 2.738 |
324 | 2218 | 13.69 | 0.466 | 18 | 3.054 | |
Erdos | 446 | 1417 | 6.33 | 0.296 | 9 | 3.952 |
USAirport | 1574 | 28,236 | 21.901 | 0.505 | 64 | 3.113 |
Yeast | 2375 | 11,693 | 9.847 | 0.306 | 40 | 5.094 |
Power | 4961 | 6964 | 2.669 | 0.080 | 5 | 18.989 |
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Yang, H.; An, S. Critical Nodes Identification in Complex Networks. Symmetry 2020, 12, 123. https://doi.org/10.3390/sym12010123
Yang H, An S. Critical Nodes Identification in Complex Networks. Symmetry. 2020; 12(1):123. https://doi.org/10.3390/sym12010123
Chicago/Turabian StyleYang, Haihua, and Shi An. 2020. "Critical Nodes Identification in Complex Networks" Symmetry 12, no. 1: 123. https://doi.org/10.3390/sym12010123
APA StyleYang, H., & An, S. (2020). Critical Nodes Identification in Complex Networks. Symmetry, 12(1), 123. https://doi.org/10.3390/sym12010123