Link prediction in dynamic networks using random dot product graphs
| Autor: | Joshua Neil, Anna S. Bertiger, Francesco Sanna Passino, Nicholas A. Heard |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Technology Computer Networks and Communications Dynamic networks MODELS TIME-SERIES Link prediction computer.software_genre Statistics - Applications Computer Science Artificial Intelligence STOCHASTIC BLOCKMODELS 0801 Artificial Intelligence and Image Processing Artificial Intelligence & Image Processing Applications (stat.AP) Adjacency matrix Adjacency spectral embedding stat.AP Social and Information Networks (cs.SI) Science & Technology Computer Science Information Systems Series (mathematics) Node (networking) 0804 Data Format Dot product Computer Science - Social and Information Networks Link (geometry) Computer Science Applications Variety (cybernetics) Task (computing) 0806 Information Systems Computer Science Random dot product graph Data mining Spectral method computer cs.SI Information Systems |
| DOI: | 10.48550/arxiv.1912.10419 |
| Popis: | The problem of predicting links in large networks is an important task in a variety of practical applications, including social sciences, biology and computer security. In this paper, statistical techniques for link prediction based on the popular random dot product graph model are carefully presented, analysed and extended to dynamic settings. Motivated by a practical application in cyber-security, this paper demonstrates that random dot product graphs not only represent a powerful tool for inferring differences between multiple networks, but are also efficient for prediction purposes and for understanding the temporal evolution of the network. The probabilities of links are obtained by fusing information at two stages: spectral methods provide estimates of latent positions for each node, and time series models are used to capture temporal dynamics. In this way, traditional link prediction methods, usually based on decompositions of the entire network adjacency matrix, are extended using temporal information. The methods presented in this article are applied to a number of simulated and real-world graphs, showing promising results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|