Streaming Network Embedding through Local Actions

11/14/2018
by   Xi Liu, et al.
0

Recently, considerable research attention has been paid to network embedding, a popular approach to construct feature vectors of vertices. Due to the curse of dimensionality and sparsity in graphical datasets, this approach has become indispensable for machine learning tasks over large networks. The majority of existing literature has considered this technique under the assumption that the network is static. However, networks in many applications, nodes and edges accrue to a growing network as a streaming. A small number of very recent results have addressed the problem of embedding for dynamic networks. However, they either rely on knowledge of vertex attributes, suffer high-time complexity or need to be re-trained without closed-form expression. Thus the approach of adapting the existing methods to the streaming environment faces non-trivial technical challenges. These challenges motivate developing new approaches to the problems of streaming network embedding. In this paper, We propose a new framework that is able to generate latent features for new vertices with high efficiency and low complexity under specified iteration rounds. We formulate a constrained optimization problem for the modification of the representation resulting from a stream arrival. We show this problem has no closed-form solution and instead develop an online approximation solution. Our solution follows three steps: (1) identify vertices affected by new vertices, (2) generate latent features for new vertices, and (3) update the latent features of the most affected vertices. The generated representations are provably feasible and not far from the optimal ones in terms of expectation. Multi-class classification and clustering on five real-world networks demonstrate that our model can efficiently update vertex representations and simultaneously achieve comparable or even better performance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/09/2019

Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

Network representation learning, as an approach to learn low dimensional...
research
10/25/2020

Even the Easiest(?) Graph Coloring Problem is not Easy in Streaming!

We study a graph coloring problem that is otherwise easy but becomes qui...
research
04/23/2018

Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Network embedding methodologies, which learn a distributed vector repres...
research
06/30/2020

Vertex guarding for dynamic orthogonal art galleries

Given an orthogonal polygon with orthogonal holes, we devise a dynamic a...
research
07/27/2019

DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding

Learning topological representation of a network in dynamic environments...
research
09/29/2016

OPML: A One-Pass Closed-Form Solution for Online Metric Learning

To achieve a low computational cost when performing online metric learni...
research
06/24/2019

Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units

We present ARU, an Adaptive Recurrent Unit for streaming adaptation of d...

Please sign up or login with your details

Forgot password? Click here to reset