Name
Title | ||
|---|---|---|
Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes. |
Abstract | ||
|---|---|---|
In a dynamic network, the neighborhood of the vertices evolve across different temporal snapshots of the network. Accurate modeling of this temporal evolution can help solve complex tasks involving real-life social and interaction networks. However, existing models for learning latent representation are inadequate for obtaining the representation vectors of the vertices for different time-stamps of a dynamic network in a meaningful way. In this paper, we propose latent representation learning models for dynamic networks which overcome the above limitation by considering two different kinds of temporal smoothness: (i) retrofitted, and (ii) linear transformation. The retrofitted model tracks the representation vector of a vertex over time, facilitating vertex-based temporal analysis of a network. On the other hand, linear transformation based model provides a smooth transition operator which maps the representation vectors of all vertices from one temporal snapshot to the next (unobserved) snapshot-this facilitates prediction of the state of a network in a future time-stamp. We validate the performance of our proposed models by employing them for solving the temporal link prediction task. Experiments on 9 real-life networks from various domains validate that the proposed models are significantly better than the existing models for predicting the dynamics of an evolving network. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Social and Information Networks | Dynamic network analysis,Data mining,Vertex (geometry),Computer science,Evolving networks,Theoretical computer science,Operator (computer programming),Linear map,Smoothness,Snapshot (computer storage),Feature learning |
DocType | Volume | Citations |
Journal | abs/1804.05816 | 2 |
PageRank | References | Authors |
0.35 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tanay Kumar Saha | 1 | 44 | 5.07 |
| Thomas Williams | 2 | 4 | 5.59 |
| Mohammad Al Hasan | 3 | 427 | 35.08 |
| Shafiq R. Joty | 4 | 560 | 56.72 |
| Nicholas K. Varberg | 5 | 2 | 0.35 |