Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

TL;DR

This paper introduces an incremental skip-gram algorithm with negative sampling for dynamic network embedding, enabling efficient updates in evolving graphs while maintaining high performance and theoretical guarantees.

Contribution

It presents a novel stochastic gradient-based method for dynamic network embedding with theoretical performance bounds and significant speedup over existing methods.

Findings

Achieves up to 22 times speedup in training time.

Maintains comparable embedding quality to static methods.

Validates theoretical analysis through extensive experiments.

Abstract

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to 22 times speedup has been achieved.