Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs

Gracious, T and Gupta, S and Kanthali, A and Castro, RM and Dukkipati, A (2021) Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 2 - 9 February 2021, Virtual, Online, pp. 4054-4062.

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Abstract

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. Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Item Type:	Conference Paper
Publication:	35th AAAI Conference on Artificial Intelligence, AAAI 2021
Publisher:	Association for the Advancement of Artificial Intelligence
Additional Information:	The copyright for this article belongs to Association for the Advancement of Artificial Intelligence.
Keywords:	Data mining; Knowledge graph; Learning systems, Dynamic network; Interaction matrices; Knowledge graphs; Latent space models; Latent vectors; matrix; Network knowledge; Temporal knowledge; Variational inference; Vector matrix, Heterogeneous networks
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	09 Mar 2023 06:24
Last Modified:	09 Mar 2023 06:24
URI:	https://eprints.iisc.ac.in/id/eprint/80880

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