Graph Embedding Method Based on Biased Walking for Link Prediction
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 influe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/20/3778 |
| _version_ | 1797471737512722432 |
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| author | Mingshuo Nie Dongming Chen Dongqi Wang |
| author_facet | Mingshuo Nie Dongming Chen Dongqi Wang |
| author_sort | Mingshuo Nie |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:52:27Z |
| format | Article |
| id | doaj.art-b7d2da182b124aa9be127036cafee667 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T19:52:27Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b7d2da182b124aa9be127036cafee6672023-11-24T01:06:52ZengMDPI AGMathematics2227-73902022-10-011020377810.3390/math10203778Graph Embedding Method Based on Biased Walking for Link PredictionMingshuo Nie0Dongming Chen1Dongqi Wang2Software College, Northeastern University, Shenyang 110169, ChinaSoftware College, Northeastern University, Shenyang 110169, ChinaSoftware College, Northeastern University, Shenyang 110169, ChinaLink 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.https://www.mdpi.com/2227-7390/10/20/3778link predictionbiased walkingtriadic closure theorynetwork clustering coefficient |
| spellingShingle | Mingshuo Nie Dongming Chen Dongqi Wang Graph Embedding Method Based on Biased Walking for Link Prediction Mathematics link prediction biased walking triadic closure theory network clustering coefficient |
| title | Graph Embedding Method Based on Biased Walking for Link Prediction |
| title_full | Graph Embedding Method Based on Biased Walking for Link Prediction |
| title_fullStr | Graph Embedding Method Based on Biased Walking for Link Prediction |
| title_full_unstemmed | Graph Embedding Method Based on Biased Walking for Link Prediction |
| title_short | Graph Embedding Method Based on Biased Walking for Link Prediction |
| title_sort | graph embedding method based on biased walking for link prediction |
| topic | link prediction biased walking triadic closure theory network clustering coefficient |
| url | https://www.mdpi.com/2227-7390/10/20/3778 |
| work_keys_str_mv | AT mingshuonie graphembeddingmethodbasedonbiasedwalkingforlinkprediction AT dongmingchen graphembeddingmethodbasedonbiasedwalkingforlinkprediction AT dongqiwang graphembeddingmethodbasedonbiasedwalkingforlinkprediction |