Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs

Tony Gracious, Shubham Gupta, Arun Kanthali, Rui M. Castro, Ambedkar Dukkipati

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAssociation for the Advancement of Artificial Intelligence
Pages4054-4062
Number of pages9
ISBN (Electronic)9781713835974
Publication statusPublished - 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - ONLINE, Virtual, Online
Duration: 2 Feb 20219 Feb 2021

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/02/219/02/21

Bibliographical note

Funding Information:
This work arose from a visit of Ambedkar Dukkipati to Eurandom, with the generous support of the STAR cluster and Eurandom. The authors TG, SG, AK and AD would like to thank the Ministry of Human Resource Development (MHRD), Government of India, for their generous funding towards this work through the UAY Project: IISc 001. SG is supported by WIPRO PhD Fellowship.

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

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