Prediction of missing links and reconstruction of complex networks

Zhang, Chengjun; Zeng, An

doi:10.1142/s0129183116501205

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…To quantitatively validate whether the proposed method ESBM accomplishes this aim, inspired by [22] and [27], we define the relative error rate (the difference rate) of the observed network as:…”

Section: B Metrics For Evaluation Performancementioning

confidence: 99%

Reconstruction of Complex Networks Under Missing and Spurious Noise Without Prior Knowledge

Ren

Song²,

Zhang

et al. 2019

IEEE Access

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The problem of network reconstruction under missing and spurious interactions has attracted widespread interests, for it currently appears in a myriad of contexts. However, the existing methods require assuming that prior knowledge is known, which is not always available in realistic situations. The approach of stochastic block models (SBMs) can overcome this limitation, but it is very time-consuming and liable to trap in a local optimum. Specifically, we have to passively wait for several days or longer when it is applied to relatively big networks. In this paper, we study the heuristic mechanism of SBM and reveal that its greedy strategy may lead to the drop of the solution speed and optimality. Accordingly, we propose a novel network reconstruction method, called enhanced SBM (ESBM), with the assistance of the simulated annealing mechanism. By comparing ESBM with SBM in both synthetic and real networks, the experimental results suggest that the reconstructed network's global properties of the ESBM can be closer to those of the true network than those of the SBM. Furthermore, the ESBM can flexibly adjust the convergence time according to the actual speed demands. We are surprised to find that, by slightly sacrificing the prediction accuracy, the calculation speed of ESBM can be more than ten times faster than that of SBM. Our results may also shed light on maximum-likelihood methods for large-scale networks' reconstruction.INDEX TERMS Complex network, link prediction, network reconstruction.

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Section: B Metrics For Evaluation Performancementioning

confidence: 99%

Reconstruction of Complex Networks Under Missing and Spurious Noise Without Prior Knowledge

Ren

Song²,

Zhang

et al. 2019

IEEE Access

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“…Link prediction is an important part of complex network analysis [1], which aims at predicting missing, spurious, or new links in the current structure of the network by using the structure information and attribute information of a given network [2]. Link prediction plays an important role in social network analysis [3,4], network reconstruction [5], and network evolution mechanisms [6][7][8]. In addition, link prediction in the theoretical analysis assists in comprehending the mechanism of propagation and diffusion of information [9].…”

Section: Introductionmentioning

confidence: 99%

Graph Embedding Method Based on Biased Walking for Link Prediction

2022

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Link prediction is an essential and challenging problem in research on complex networks, which can provide research tools and theoretical supports for the formation and evolutionary mechanisms of networks. Existing graph representation learning methods based on random walks usually ignore the influence of local network topology on the transition probability of walking nodes when predicting the existence of links, and the sampling strategy of walking nodes during random walks is uncontrolled, which leads to the inability of these methods to effectively learn high-quality node vectors to solve the link prediction problem. To address the above challenges, we propose a novel graph embedding method for link prediction. Specifically, we analyze the evolution mechanism of links based on triadic closure theory and use the network clustering coefficient to represent the aggregation ability of the network’s local structure, and this adaptive definition of the aggregation ability of the local structure enables control of the walking strategy of nodes in the random walking process. Finally, node embedding generated based on biased walking paths is employed to solve the link prediction problem. Extensive experiments and analyses show that the TCW algorithm provides high accuracy across a diverse set of datasets.

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“…A promising way to solve the problem of information overload is through recommender systems [ 5 – 8 ], which recommend information and products to users according to their previous behavior records. Compared with search engines, recommender systems make predictions based on the analysis of users’ interest preferences [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the robustness of recommender systems against spammers

Chen

Liu

et al. 2018

PLoS ONE

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The accuracy and diversity of recommendation algorithms have always been the research hotspot of recommender systems. A good recommender system should not only have high accuracy and diversity, but also have adequate robustness against spammer attacks. However, the issue of recommendation robustness has received relatively little attention in the literature. In this paper, we systematically study the influences of different spammer behaviors on the recommendation results in various recommendation algorithms. We further propose an improved algorithm by incorporating the inner-similarity of user’s purchased items in the classic KNN approach. The new algorithm effectively enhances the robustness against spammer attacks and thus outperforms traditional algorithms in recommendation accuracy and diversity when spammers exist in the online commercial systems.

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Prediction of missing links and reconstruction of complex networks

Cited by 7 publications

References 28 publications

Reconstruction of Complex Networks Under Missing and Spurious Noise Without Prior Knowledge

Reconstruction of Complex Networks Under Missing and Spurious Noise Without Prior Knowledge

Graph Embedding Method Based on Biased Walking for Link Prediction

Enhancing the robustness of recommender systems against spammers

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