Using link and content over time for embedding generation in Dynamic Attributed Networks

TL;DR

This paper introduces Chimera, a shared factorization model that integrates link, content, and temporal data to generate embeddings for evolving networks, enabling community detection, analysis, and future community prediction.

Contribution

The work presents a novel multidimensional embedding approach that captures temporal dynamics in attributed networks, improving community analysis and prediction over static methods.

Findings

Effective in capturing dynamic community changes

Improves community prediction accuracy

Simplifies temporal network analysis

Abstract

In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks. Such temporal and content-rich networks occur in many real-life settings, such as bibliographic networks and question answering forums. Most of the work in the literature (that uses both content and structure) deals with static snapshots of networks, and they do not reflect the dynamic changes occurring over multiple snapshots. Incorporating dynamic changes in the communities into the analysis can also provide useful insights about the changes in the network such as the migration of authors across communities. In this work, we propose Chimera, a shared factorization model that can simultaneously account for graph links, content, and temporal analysis. This approach works by extracting the latent semantic structure of the network in multidimensional form, but in a way that takes into account the temporal continuity of these embeddings. Such an approach simplifies temporal analysis of the underlying network by using the embedding as a surrogate. A consequence of this simplification is that it is also possible to use this temporal sequence of embeddings to predict future communities. We present experimental results illustrating the effectiveness of the approach.