Link Prediction based on Quantum-Inspired Ant Colony Optimization

Cao, Zhiwei; Zhang, Yichao; Guan, Jihong; Zhou, Shuigeng

doi:10.1038/s41598-018-31254-3

Cited by 14 publications

(6 citation statements)

References 40 publications

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“…The scale of the information used in quasi-local algorithms is between local and global algorithms. Typical quasi-local algorithms include the local random walk algorithm (LRW), local path algorithm (LP), Propflow algorithm [37], and Quantum-inspired Ant Colony Optimization (QACO) algorithm [38]. The information used in the quasi-local algorithms is normally less than the global algorithms, while their performances are often promising.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

“…Therefore, it is selected as a component of the BGE model. b) QACO Algorithm: As a representative quasi-local algorithm, the QACO algorithm integrates ant colony optimization and quantum computing [38]. Consider an undirected network G = (V, E), and let a number of artificial ants randomly diffuse in G, where each node and node pair are respectively allocated a certain amount of pheromone.…”

Section: B Interaction Predictionmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Graph Embedding Model for Link-Based Classification Problems

Zhang

Zhuang

Liu

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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In recent years, the analysis of human interaction data has led to the rapid development of graph embedding methods. Topological information is typically interpreted into embedded vectors or convolution kernels for link-based classification problems. This paper introduces a Bayesian graph embedding model for such problems, integrating network reconstruction, link prediction, and behavior prediction into a unified framework. Unlike the existing graph embedding methods, this model does not embed the topology of nodes or links into a low-dimensional space but sorts the probabilities of upcoming links and fuses the information of node topology and data domain via sorting. The new model integrates supervised transaction predictors with unsupervised link prediction models, summarizing local and global topological information. The experimental results on a financial trading dataset and a retweet network dataset demonstrate that the proposed feature fusion model outperforms the tested benchmarked machine learning algorithms in precision, recall, and F1-measure. The proposed learning structure has a fundamental methodological contribution and can be extended and applied to various link-based classification problems in different fields.

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Section: Preliminaries and Related Workmentioning

confidence: 99%

Section: B Interaction Predictionmentioning

confidence: 99%

A Bayesian Graph Embedding Model for Link-Based Classification Problems

Zhang

Zhuang

Liu

et al. 2022

IEEE Trans. Netw. Sci. Eng.

Self Cite

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show abstract

“…Lately, ACO has been improved and used to solve different problems more efficiently. For example, for automated guided vehicles (Li et al, 2020), for topology-based link prediction (Cao et al, 2018) and for query optimization (Mohsin et al, 2021). These implementations are based on the parallel nature of quantum systems.…”

Section: Previous Workmentioning

confidence: 99%

Implementable Hybrid Quantum Ant Colony Optimization Algorithm

de Andoin,

Echanobe

2021

Preprint

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We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to solve NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codes only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths using Fredkin gates without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.

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“…2 For example, one of the optimization challenges posed by fireflies, ants, bees, harris hawks, gray wolf optimize, spider monkey, elephant herding, and birds is their social characteristics; these characteristics have been modeled by behavior-based models. [3][4][5][6][7][8][9][10][11][12] These societies were selected as an example for the development of similar behaviors of several scientific applications. 12,13 Kennedy and Eberhart 14 introduced a stochastic method for reaching an optimal goal called particle swarm optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

Garra Rufa‐inspired optimization technique

Jaber¹,

Abdulbari

Shalash³

et al. 2020

Int J Intell Syst

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Natural selection has inspired researchers to develop and apply several intelligent optimization techniques in the past few decades. Generally, in artificial intelligence optimization, the particles follow a local or global best particle until finding an acceptable solution. In well‐ developed optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. The present work was inspired by the fascinating movement of Garra Rufa fish between two immersed legs during a regular “fish massage session.” A new optimization approach is proposed and modeled based on the movements of Garra Rufa fish, in which the particles are separated into groups, and the best optimal value leads each group for the group. Also, some of these particles are allowed to change groups depending on the fitness of the leaders of the groups. The suggested strategy is then compared with PSO and FA using multiple test optimization functions, such as the Ackley, Hartmann, Michalewicz, Shubert, Easom, Bohachevsky, and Rastrigin functions. Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.

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Link Prediction based on Quantum-Inspired Ant Colony Optimization

Cited by 14 publications

References 40 publications

A Bayesian Graph Embedding Model for Link-Based Classification Problems

A Bayesian Graph Embedding Model for Link-Based Classification Problems

Implementable Hybrid Quantum Ant Colony Optimization Algorithm

Garra Rufa‐inspired optimization technique

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