Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction
Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge fe...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/1277579 |
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| _version_ | 1832566462512889856 |
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| author | Chuanting Zhang Ke-Ke Shang Jingping Qiao |
| author_facet | Chuanting Zhang Ke-Ke Shang Jingping Qiao |
| author_sort | Chuanting Zhang |
| collection | DOAJ |
| description | Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge features or node similarity have been proposed to solve the link prediction problem, many technical challenges still exist due to the unique structural properties of networks, especially when the networks are sparse. From the graph mining perspective, we first give empirical evidence of the inconsistency between heuristic and learned edge features. Then, we propose a novel link prediction framework, AdaSim, by introducing an Adaptive Similarity function using features obtained from network embedding based on random walks. The node feature representations are obtained by optimizing a graph-based objective function. Instead of generating edge features using binary operators, we perform link prediction solely leveraging the node features of the network. We define a flexible similarity function with one tunable parameter, which serves as a penalty of the original similarity measure. The optimal value is learned through supervised learning and thus is adaptive to data distribution. To evaluate the performance of our proposed algorithm, we conduct extensive experiments on eleven disparate networks of the real world. Experimental results show that AdaSim achieves better performance than state-of-the-art algorithms and is robust to different sparsities of the networks. |
| format | Article |
| id | doaj-art-22f70b50c8954428a7b69aa02e6f2d39 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-22f70b50c8954428a7b69aa02e6f2d392025-02-03T01:04:04ZengWileyComplexity1099-05262021-01-01202110.1155/2021/1277579Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link PredictionChuanting Zhang0Ke-Ke Shang1Jingping Qiao2Computer, Electrical and Mathematical Science Engineering DivisionComputational Communication CollaboratorySchool of Information Science and EngineeringLink prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge features or node similarity have been proposed to solve the link prediction problem, many technical challenges still exist due to the unique structural properties of networks, especially when the networks are sparse. From the graph mining perspective, we first give empirical evidence of the inconsistency between heuristic and learned edge features. Then, we propose a novel link prediction framework, AdaSim, by introducing an Adaptive Similarity function using features obtained from network embedding based on random walks. The node feature representations are obtained by optimizing a graph-based objective function. Instead of generating edge features using binary operators, we perform link prediction solely leveraging the node features of the network. We define a flexible similarity function with one tunable parameter, which serves as a penalty of the original similarity measure. The optimal value is learned through supervised learning and thus is adaptive to data distribution. To evaluate the performance of our proposed algorithm, we conduct extensive experiments on eleven disparate networks of the real world. Experimental results show that AdaSim achieves better performance than state-of-the-art algorithms and is robust to different sparsities of the networks.http://dx.doi.org/10.1155/2021/1277579 |
| spellingShingle | Chuanting Zhang Ke-Ke Shang Jingping Qiao Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction Complexity |
| title | Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction |
| title_full | Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction |
| title_fullStr | Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction |
| title_full_unstemmed | Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction |
| title_short | Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction |
| title_sort | adaptive similarity function with structural features of network embedding for missing link prediction |
| url | http://dx.doi.org/10.1155/2021/1277579 |
| work_keys_str_mv | AT chuantingzhang adaptivesimilarityfunctionwithstructuralfeaturesofnetworkembeddingformissinglinkprediction AT kekeshang adaptivesimilarityfunctionwithstructuralfeaturesofnetworkembeddingformissinglinkprediction AT jingpingqiao adaptivesimilarityfunctionwithstructuralfeaturesofnetworkembeddingformissinglinkprediction |