Abstract: The popular Random Dot Product Graph (RDPG) generative model postulates that each node has an associated (latent) vector, and the probability of ...
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As- sume we are monitoring a stream of graphs and the objective is to track the latent vectors. Examples include recommender systems or monitoring of a wireless ...
Q Isn't this the classic problem of recursively updating eigenvalues/vectors? A Yes, but. 7 Computationally expensive except for specific types of changes ...
Q: Scalability for large graphs? Streaming settings for dynamic graphs? Missing data in A? • Gradient descent (GD) approach: Estimate Xt+1 = Xt − α∇ ...
This work develops an iterative algorithm that updates the latent vectors' estimation as new graphs from the stream arrive, and does not accumulate errors ...
All in all, relative to prior art our RDPG embedding framework offers a better representation at a competitive computational cost, and it is applicable to ...
Tracking the Adjacency Spectral Embedding for Streaming Graphs
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Mar 22, 2023 · The popular Random Dot Product Graph (RDPG) generative model postulates that each node has an associated (latent) vector, ...
Nov 16, 2022 · Presented by Gonzalo Mateos (University of Rochester) for the Data sciEnce on GrAphS (DEGAS) Webinar Series, in conjunction with the IEEE ...
Missing: Tracking Streaming
We show the algorithms are scalable, robust to missing network data, and can track the latent positions over time when the graphs are acquired in a streaming ...