FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings

Abstract

Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on graphs have centered on both homogeneous and heterogeneous graphs, where the latter presenting the challenges associated with multi-typed nodes and/or edges. In this paper, we consider the additional challenge of evolving graphs. We ask the question of whether the advances in representation learning for static graphs can be leveraged for dynamic graphs and how? It is important to be able to incorporate those advances to maximize the utility and generalization of methods. To that end, we propose the Framework for Incremental Learning of Dynamic Networks Embedding (FILDNE), which can utilize any existing static representation learning method for learning node embeddings, while keeping the computational costs low. FILDNE integrates the feature vectors computed using the standard methods over different timesteps into a single representation by developing a convex combination function and alignment mechanism. Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to the contemporary methods for representation learning on dynamic graphs.

Publication
arXiv preprint
Piotr Bielak
Piotr Bielak
PhD Student

My research interests include graph representation learning and large scale data processing.

Maciej Falkiewicz
Maciej Falkiewicz
PhD Student

My research interests are Representation Learning and Geometric Deep Learning.

Tomasz Kajdanowicz
Tomasz Kajdanowicz
Associate Professor, head of faculty’s doctoral studies, head of department

My research interests include representation learning, social network and media analysis, and machine learning.

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