  Although static networks have been extensively studied in machine learning,
data mining, and AI communities for many decades, the study of dynamic networks
has recently taken center stage due to the prominence of social media and its
effects on the dynamics of social networks. In this paper, we propose a
statistical model for dynamically evolving networks, together with a
variational inference approach. Our model, Neural Latent Space Model with
Variational Inference, encodes edge dependencies across different time
snapshots. It represents nodes via latent vectors and uses interaction matrices
to model the presence of edges. These matrices can be used to incorporate
multiple relations in heterogeneous networks by having a separate matrix for
each of the relations. To capture the temporal dynamics, both node vectors and
interaction matrices are allowed to evolve with time. Existing network analysis
methods use representation learning techniques for modelling networks. These
techniques are different for homogeneous and heterogeneous networks because
heterogeneous networks can have multiple types of edges and nodes as opposed to
a homogeneous network. Unlike these, we propose a unified model for homogeneous
and heterogeneous networks in a variational inference framework. Moreover, the
learned node latent vectors and interaction matrices may be interpretable and
therefore provide insights on the mechanisms behind network evolution. We
experimented with a single step and multi-step link forecasting on real-world
networks of homogeneous, bipartite, and heterogeneous nature, and demonstrated
that our model significantly outperforms existing models.
