DyANE: Dynamics-aware node embedding for temporal networks

Abstract : Low-dimensional vector representations of network nodes have proven successful to feed graph data to machine learning algorithms and to improve performance across diverse tasks. Most of the embedding techniques, however, have been developed with the goal of achieving dense, low-dimensional encoding of network structure and patterns. Here, we present a node embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporal networks-rather than of the network structure itself-with the goal of enabling prediction tasks related to the evolution and outcome of these processes. We achieve this by using a modified supra-adjacency representation of temporal networks and building on standard embedding techniques for static graphs based on random-walks. We show that the resulting embedding vectors are useful for prediction tasks related to paradigmatic dynamical processes, namely epidemic spreading over empirical temporal networks. In particular, we illustrate the performance of our approach for the prediction of nodes' epidemic states in a single instance of the spreading process. We show how framing this task as a supervised multi-label classification task on the embedding vectors allows us to estimate the temporal evolution of the entire system from a partial sampling of nodes at random times, with potential impact for nowcasting infectious disease dynamics.
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02289376
Contributor : Alain Barrat <>
Submitted on : Monday, September 16, 2019 - 4:14:46 PM
Last modification on : Thursday, September 19, 2019 - 1:21:14 AM
Long-term archiving on: Saturday, February 8, 2020 - 12:13:55 PM

File

1909.05976.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02289376, version 1
  • ARXIV : 1909.05976

Collections

Citation

Koya Sato, Mizuki Oka, Alain Barrat, Ciro Cattuto. DyANE: Dynamics-aware node embedding for temporal networks. 2019. ⟨hal-02289376⟩

Share

Metrics

Record views

60

Files downloads

50