A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation
Abstract
:1. Introduction
1.1. Delayed Competition Propagation Overview
1.2. Delayed Competition Propagation Problem
1.3. Main Work of This Paper
2. Related Work
2.1. Delayed Competition Study Based on an Economic Model
2.2. Delayed Competition Study Based on the Disease Model
2.3. Delayed Competition Study Based on the Information Model
3. The Method Proposed in This Paper
3.1. Main Ideas
3.2. Variable Representation
3.3. Node Evaluation
3.3.1. Find the Nearest Propagation Region
3.3.2. Build the Shortest Path Snapshot
3.3.3. Evaluate Competitive Influence Factors
3.4. Algorithm Definition
3.4.1. Node Evaluation Method Based on Shortest Path Snapshot
3.4.2. Algorithm Input and Output Pseudo-Code
Algorithm 1: HeuPFE, a heuristic finding algorithm based on propagation factor evaluation |
Input: Graph: G, the number of negative seeds: kN Output: Top-kP vertices 1: initialize S = Ø, SG = Ø 2: select kN vertices as the negatives seeds 3: community partition and get z candidate communities which contain negative seed vertices: C1, C2, …, Cz 4: for i = 1 to z do 5: in current community Ci, Sv = 0 6: construct the vertex pair index of primitive topology, and add to the array AP 7: for j = 1 to r do 8: disturb randomly the sequence of primitive topology, and add to the array AS 9: generate the adjacency matrix of array AS, and add to the matrix M 10: find the shortest path snapshot from the vertex u, and add to the queue Q 11: assess the PFE of every v for negative vertex u based on the shortest path snapshot which is based on Formula (4) then Sv+ = PFE (v,u) 12: end for 13: compute average PFE for every vertex v except the negative vertices: PFE(v,u) = Sv/r 14: for j = 1 to t do 15: SP = SP ∪ {argmax v ϵ Ci\SP {PFE(v,u)}} 16: end for 17: end for 18: return SP |
3.4.3. Introduction of Each Function of the Algorithm
4. Experimental Analysis
4.1. Experimental Setup
4.1.1. Experimental Environment
4.1.2. Experimental Dataset
4.1.3. Experimental Model
4.1.4. Comparison Method
4.1.5. Evaluation Indicators
4.2. Analysis of Results
4.2.1. Experimental Results Demonstration
4.2.2. Analysis of Influence Scope
4.2.3. Running Time Analysis
5. Conclusions and Outlook
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Descriptions |
---|---|
N | The number of vertices |
M | The number of edges |
SN | The set of negative seed |
kN | The number of negative seeds |
kP | The number of positive seeds |
R | The simulation times of propagation in algorithm |
R | The simulation times of matrix in algorithm |
SimCas(S) | The spread process of set S |
S | The set of selected seed |
SG | The candidate vertices set for algorithm |
MGv | The marginal gain of vertex v which is added to set S |
PFEv | The propagation factor evaluation of vertex v |
Z | The number of communities |
NC | The number of vertices in community |
MC | The number of edges in community |
Nsd | The shortest distance from the negative vertex u to v |
Spf | The shortest path frequency through vertex v |
Adm | The adjacency degree measure of vertex v |
DataSet | #Vertices | #Edges |
---|---|---|
Dblp | 14,485 | 37,026 |
GrQc | 5242 | 14,485 |
Hep | 15,233 | 31,380 |
Phy | 14,997 | 57,866 |
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Li, Y.; Wang, Z. A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation. Information 2024, 15, 370. https://doi.org/10.3390/info15070370
Li Y, Wang Z. A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation. Information. 2024; 15(7):370. https://doi.org/10.3390/info15070370
Chicago/Turabian StyleLi, Yang, and Zhiqiang Wang. 2024. "A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation" Information 15, no. 7: 370. https://doi.org/10.3390/info15070370