Link Prediction in Social Networks: An Edge Creation History-Retrieval Based Method that Combines Topological and Contextual Data
| Autor: | Ronaldo R. Goldschmidt, Claudia Marcela Justel, Argus A. B. Cavalcante |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Intelligent Systems ISBN: 9783030613792 BRACIS (2) |
| DOI: | 10.1007/978-3-030-61380-8_26 |
| Popis: | Link prediction is an online social network (OSN) analysis task whose objective is to identify pairs of non-connected nodes with a high probability of getting connected in the near future. Recently, proposed link prediction methods consider topological data from OSN past states (i.e., snapshots that depict the network structure at certain moments in the past). Although past states-based methods retrieve information that describes how the network’s topology was at the events of link emergence (i.e., moments when the existing edges were created), they do not take into account contextual data concerning those events. Hence, they take the chance to disregard information about the circumstances that may have influenced the appearance of old edges, and that could be useful to predict the creation of new ones. To remedy this issue, this work extends a past states-based method to retrieve both topological and contextual data from the events of edge emergence and combine them to predict links. The extended method presented promising results on experimental data. Overall, it overcame the original method in five different scenarios from five co-authorship OSN frequently used for link prediction method evaluation. |
| Databáze: | OpenAIRE |
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