Research map / Learning theory and algorithms / Submodular and game algorithms
DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)
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Proposes DQSSA, a discretized quantum-inspired salp swarm algorithm with Louvain community detection and a Local Influence Estimator, for influence maximization on four real-world social networks.
Problem
Influence maximization in social networks: selecting seed nodes that maximize influence spread while balancing execution time, with meta-heuristics prone to premature convergence.
Approach
Combines salp swarm algorithm with quantum-inspired reverse learning and quantum mutation, discretizes it for node selection, and applies it within Louvain communities scored by a Local Influence Estimator.
Key results
- DQSSA reports lower execution time than DQBA, DQPSO, DBA and DPSO on Jazz (1.66 vs 2.39, 1.94, 28.04, 7.85), and on Wiki-Vote (4.91 vs 5.98, 13.37, 70.29, 36.54).
- On email-univ, DQSSA's execution time (64.32) is higher than DQBA (23.46), contrary to its general efficiency claim.
- The paper claims DQSSA surpasses all baselines in influence spread, but no quantitative FIS numbers are given in the text.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DQSSA instead of the fastest listed baseline, DQPSO | DQSSA · Jazz | execution time: 1.66 vs 1.94 | Helped |
| Use DQSSA instead of the fastest listed baseline, DQBA | DQSSA · Wiki-Vote | execution time: 4.91 vs 5.98 | Helped |
| Use DQSSA instead of the fastest listed baseline, DQPSO | DQSSA · Email-Univ | execution time: 64.32 vs 49.87 | Hurt |
| Use DQSSA instead of the fastest listed baseline, DQBA | DQSSA · Hamsterster | execution time: 25.88 vs 12.09 | Hurt |
Surprising findings
- DQSSA's execution time on email-univ (64.32) is higher than DQBA's (23.46) and on Hamsterster (25.88) is higher than DQBA's (12.09), contradicting the claim of consistent time superiority.
Limitations
- Influence spread results are reported only qualitatively in the text; FIS figures are in plots not included in the extracted text.
- Evaluation limited to four small-to-medium datasets and a single infection probability (0.1).
- Execution time is not uniformly better than all baselines.
Experimental setup
Datasets: Jazz soc-Wiki-Vote email-univ soc-hamsterster
Benchmarks: Jazz soc-Wiki-Vote email-univ soc-hamsterster
Metrics: Final Infected Scale LIE function value execution time
Baselines: H-index Gateway Local Rank PageRank Extended Neighbourhood Coreness Discrete Bat Algorithm Discrete Particle Swarm Optimisation Discrete Quantum Particle Swarm Optimisation Discrete Quantum Bat Algorithm
Builds on: Salp Swarm Algorithm Louvain algorithm Independent Cascade model
Closest papers by meaning
- Maximizing Influence in an Unknown Social Network (AAAI 2018 · similarity 0.84)
- Motif-oriented influence maximization for viral marketing in large-scale social networks (NeurIPS 2024 · similarity 0.82)
- Hyperparametric Robust and Dynamic Influence Maximization (AAAI 2025 · similarity 0.81)
- Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm (NeurIPS 2026 · similarity 0.81)
- REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization (AAAI 2025 · similarity 0.81)
- Graph Bayesian Optimization for Multiplex Influence Maximization (AAAI 2024 · similarity 0.81)
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