Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs
Keywords:Graph Mining, Social Network Analysis & Community, Representation Learning, Graph-based Machine Learning, Linked Open Data, Knowledge Graphs & KB Completio
AbstractAlthough 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.
How to Cite
Gracious, T., Gupta, S., Kanthali, A., Castro, R. M., & Dukkipati, A. (2021). Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4054-4062. https://doi.org/10.1609/aaai.v35i5.16526
AAAI Technical Track on Data Mining and Knowledge Management