  Researchers of temporal networks (e.g., social networks and transaction
networks) have been interested in mining dynamic patterns of nodes from their
diverse interactions.
  Inspired by recently powerful graph mining methods like skip-gram models and
Graph Neural Networks (GNNs), existing approaches focus on generating temporal
node embeddings sequentially with nodes' sequential interactions.
  However, the sequential modeling of previous approaches cannot handle the
transition structure between nodes' neighbors with limited memorization
capacity.
  Detailedly, an effective method for the transition structures is required to
both model nodes' personalized patterns adaptively and capture node dynamics
accordingly.
  In this paper, we propose a method, namely Transition Propagation Graph
Neural Networks (TIP-GNN), to tackle the challenges of encoding nodes'
transition structures.
  The proposed TIP-GNN focuses on the bilevel graph structure in temporal
networks: besides the explicit interaction graph, a node's sequential
interactions can also be constructed as a transition graph.
  Based on the bilevel graph, TIP-GNN further encodes transition structures by
multi-step transition propagation and distills information from neighborhoods
by a bilevel graph convolution.
  Experimental results over various temporal networks reveal the efficiency of
our TIP-GNN, with at most 7.2\% improvements of accuracy on temporal link
prediction.
  Extensive ablation studies further verify the effectiveness and limitations
of the transition propagation module.
  Our code is available at \url{https://github.com/doujiang-zheng/TIP-GNN}.
