English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Although static networks have been extensively studied in machine learning,data mining, and AI communities for many decades, the study of dynamic networkshas recently taken center stage due to the prominence of social media and itseffects on the dynamics of social networks. In this paper, we propose astatistical model for dynamically evolving networks, together with avariational inference approach. Our model, Neural Latent Space Model withVariational Inference, encodes edge dependencies across different timesnapshots. It represents nodes via latent vectors and uses interaction matricesto model the presence of edges. These matrices can be used to incorporatemultiple relations in heterogeneous networks by having a separate matrix foreach of the relations. To capture the temporal dynamics, both node vectors andinteraction matrices are allowed to evolve with time. Existing network analysismethods use representation learning techniques for modelling networks. Thesetechniques are different for homogeneous and heterogeneous networks becauseheterogeneous networks can have multiple types of edges and nodes as opposed toa homogeneous network. Unlike these, we propose a unified model for homogeneousand heterogeneous networks in a variational inference framework. Moreover, thelearned node latent vectors and interaction matrices may be interpretable andtherefore provide insights on the mechanisms behind network evolution. Weexperimented with a single step and multi-step link forecasting on real-worldnetworks of homogeneous, bipartite, and heterogeneous nature, and demonstratedthat our model significantly outperforms existing models.

Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs