weg2vec: Event embedding for temporal networks

TL;DR

weg2vec is a novel event embedding method for temporal networks that captures complex temporal and structural similarities, enabling better understanding and prediction of dynamic processes.

Contribution

It introduces weg2vec, a new approach for embedding events in temporal networks, addressing the limitations of static models and capturing complex temporal dynamics.

Findings

Successfully captures latent structures in temporal networks

Enables prediction of spreading process outcomes

Outperforms static embedding methods

Abstract

Network embedding techniques are powerful to capture structural regularities in networks and to identify similarities between their local fabrics. However, conventional network embedding models are developed for static structures, commonly consider nodes only and they are seriously challenged when the network is varying in time. Temporal networks may provide an advantage in the description of real systems, but they code more complex information, which could be effectively represented only by a handful of methods so far. Here, we propose a new method of event embedding of temporal networks, called weg2vec, which builds on temporal and structural similarities of events to learn a low dimensional representation of a temporal network. This projection successfully captures latent structures and similarities between events involving different nodes at different times and provides ways to predict the final outcome of spreading processes unfolding on the temporal structure.