Link Prediction Based on Gravitational Field of Complex Network

Yang, Yanlin; Ye, Zhonglin; Zhao, Haixing; Meng, Lei

doi:10.1109/dasc/picom/cbdcom/cyberscitech.2019.00100

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…Rahman et al [7] considered user activities and common neighbours, and defined the local and global link prediction algorithm to evaluate the similarity between nodes. Yang et al [8] introduced the concept of gravitational field and proposed the LP-GFCN algorithm based on indirect and direct gravitational. Shang et al [9] discussed the role of link direction in link prediction problems.…”

Section: A Similarity Index-based Prediction Methodsmentioning

confidence: 99%

“…If the number is very large, the complexity of the model will increase, and the model will easily over-fit. At present, the number of nodes in the DBN hidden layer is usually determined by an empirical formula, and the common empirical formulas are shown in (7), (8) and (9).…”

Section: ) the Number Of Nodes In The Hidden Layermentioning

confidence: 99%

“…where S is the number of nodes in the hidden layer, m is the input length of the model, n is the output length of the model, and a is an integer within [1], [10]. Considering the size of the sample set and reducing the time complexity of model training, this paper selects equation (8) to determine the number of nodes in the hidden layer.…”

Section: ) the Number Of Nodes In The Hidden Layermentioning

confidence: 99%

See 2 more Smart Citations

A Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network

2020

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Link prediction is to estimate the possibility of future links among nodes by utilizing known information such as network topology and node attributes. According to the characteristics of opportunistic networks (topological time-variation, node mobility and intermittent connections), this paper proposes a novel link prediction approach (IRWR-DBN) for opportunistic networks that is based on random walk and a deep belief network. First, we reconstruct the Markov probability transition matrix and define a similarity index-improved random walk with restart (IRWR). Second, we divide the opportunistic network into network snapshots. Then, the similarity matrix of each snapshot is calculated by using the IRWR index to construct a sample set. Finally, a predictive model is constructed based on a deep belief network which extracts the time-domain characteristics in the process of dynamic evolution of the opportunistic network. The experimental results on the ITC and MIT Reality datasets show that compared with methods, such as the similarity-based index (CN, AA, Katz, RA, RWR), convolutional neural network, and recurrent neural network, the proposed method is more accurate and stable. INDEX TERMS Opportunistic network, link prediction, random walk with restart, deep belief network, similarity index.

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Section: A Similarity Index-based Prediction Methodsmentioning

confidence: 99%

Section: ) the Number Of Nodes In The Hidden Layermentioning

confidence: 99%