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Susceptible-infected-spreading-based network embedding in static and temporal networks

  • Zhan, Xiu-Xiu1
  • Li, Ziyu1
  • Masuda, Naoki2, 2
  • Holme, Petter3
  • Wang, Huijuan1
  • 1 Delft University of Technology, Mekelweg 4, Delft, 2628 CD, The Netherlands , Delft (Netherlands)
  • 2 University at Buffalo, State University of New York, Buffalo, New York, NY 14260-2900, USA , New York (United States)
  • 3 Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, 226-8503, Japan , Yokohama (Japan)
Published Article
EPJ Data Science
Springer Berlin Heidelberg
Publication Date
Oct 16, 2020
DOI: 10.1140/epjds/s13688-020-00248-5
Springer Nature


Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task. Nodes with similar embedding vectors are more likely to be connected. Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths. The node pair set is further used as the input for a Skip-Gram model, a representative language model that embeds nodes (which are regarded as words) into vectors. In the present study, we propose to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths. Specifically, we propose two susceptible-infected-spreading-based algorithms, i.e., Susceptible-Infected Network Embedding (SINE) on static networks and Temporal Susceptible-Infected Network Embedding (TSINE) on temporal networks. The performance of our algorithms is evaluated by the missing link prediction task in comparison with state-of-the-art static and temporal network embedding algorithms. Results show that SINE and TSINE outperform the baselines across all six empirical datasets. We further find that the performance of SINE is mostly better than TSINE, suggesting that temporal information does not necessarily improve the embedding for missing link prediction. Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms. The better performance of SINE and TSINE requires a smaller sampling size in comparison with the baseline algorithms. Hence, SI-spreading-based embedding tends to be more applicable to large-scale networks.

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