Name
Abstract | ||
|---|---|---|
Social platforms such as Weibo, Facebook and Twitter have become a part of daily life, where people can exchange information. In this process, people's behaviors often influence each other. Social influence prediction has become one of the hot issues at present. In this paper, NNMLInf social influence prediction model is constructed based on neural network multi-label classification. People's network structure features are taken as the network input, and their behaviors are divided into multiple labels as the network output. Node2vec is adopted to extract network representative features of users. This model combines the network structure with human behaviors, and the prediction results can be more practical. The experiment carried on BlogCatalog, Flickr and Youtube shows that NNMLInf model performs better than traditional approaches such as DT (decision tree), SVM (support vector machine), and better expresses social influence .
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3321408.3321409 | Proceedings of the ACM Turing Celebration Conference - China |
Keywords | Field | DocType |
multi-label classification, neural network, social influence, social network | Computer science,Multi-label classification,Social influence,Artificial intelligence,Artificial neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-7158-2 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xupeng Wang | 1 | 9 | 9.33 |
| Zhongwen Guo | 2 | 116 | 13.32 |
| Xi Wang | 3 | 11 | 3.98 |
| Shiyong Liu | 4 | 4 | 2.44 |
| Wei Jing | 5 | 0 | 0.34 |
| Yuan Liu | 6 | 0 | 0.34 |