Adversarial Network Embedding (1711.07838v1)

Abstract: Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization. Existing methods can effectively encode different structural properties into the representations, such as neighborhood connectivity patterns, global structural role similarities and other high-order proximities. However, except for objectives to capture network structural properties, most of them suffer from lack of additional constraints for enhancing the robustness of representations. In this paper, we aim to exploit the strengths of generative adversarial networks in capturing latent features, and investigate its contribution in learning stable and robust graph representations. Specifically, we propose an Adversarial Network Embedding (ANE) framework, which leverages the adversarial learning principle to regularize the representation learning. It consists of two components, i.e., a structure preserving component and an adversarial learning component. The former component aims to capture network structural properties, while the latter contributes to learning robust representations by matching the posterior distribution of the latent representations to given priors. As shown by the empirical results, our method is competitive with or superior to state-of-the-art approaches on benchmark network embedding tasks.

• The paper introduces an adversarial framework that significantly improves the robustness of network embeddings in noisy environments.
• It integrates a structure preserving inductive DeepWalk variant with a GAN-inspired generator-discriminator setup.
• Empirical tests on Cora, Citeseer, and Wiki datasets show enhanced performance in node classification and network visualization.

Adversarial Network Embedding: A Comprehensive Overview

Graph representation learning, also termed as network embedding, has been widely recognized for its efficacy in tasks such as node classification, link prediction, and network visualization. The paper "Adversarial Network Embedding" by Quanyu Dai et al. introduces an approach leveraging adversarial training paradigms to enhance the robustness of graph representations. The proposed Adversarial Network Embedding (ANE) framework uniquely integrates a structure preserving component and an adversarial learning component, advancing the capabilities of traditional embedding methods in maintaining robustness particularly in noisy environments.

The ANE model is premised on the insight that while existing methods like DeepWalk, LINE, and node2vec excel at capturing various structural aspects of networks, they often lack constraints that ensure robustness. These models predominantly focus on encoding structural properties such as neighborhood connectivity and global role similarities but may falter under noisy conditions common in real-world datasets. The introduction of adversarial learning effectively acts as a regularization mechanism, enforcing a degree of stability by mapping the latent feature space of network representations to a predefined prior distribution.

The structure preserving component anchors on an inductive variant of DeepWalk, referred to as Inductive DeepWalk (IDW), in the paper. IDW employs parameterized functions to generate low-dimensional embeddings effectively, thus allowing the adoption of adversarial training without reliance on embedding lookups. This design allows models to potentially explore richer, non-linear network structures, significant for emerging applications where networks are complemented with node attributes.

The adversarial learning component introduces a generator and a discriminator, a classic two-player adversarial setup designed to improve robustness. The generator learns to make embedding representations indistinguishable from samples drawn from a prior distribution, while the discriminator aims to distinguish between true prior samples and embeddings. This adversarial objective is rooted in the GAN framework, fostering representations less susceptible to variations and inconsistencies inherent in graph data.

Empirical results underscore the merits of the ANE framework. In visualization tasks and node classification on datasets like Cora, Citeseer, and Wiki, ANE consistently showcases superior resilience and interpretative power relative to traditional models. For instance, on node classification tasks, ANE's Adversarial Inductive DeepWalk (AIDW) exhibits notable improvements over baselines across varying labeled-training ratios, reflecting its robustness and ability to generalize from partial data access.

A critical implication of this research pertains to the enhanced performance in unsupervised representation fitting within graphs, specifically under adversarial settings. The frameworks' ability to enforce a structured probabilistic mapping ensures robustness in representation learning that is crucial for real-world applications, where noise and non-linear relationships are prevalent. Furthermore, given ANE’s adaptable nature, future explorations might consider its extension to incorporate more complex models, thereby broadening potential applicability across diverse domains, from social networks to bioinformatics.

In conclusion, the adversarial approach to network embedding as detailed in this paper signifies a substantial advancement in the pursuit of robust graph representations. Through the integration of a dual-component system, the paper not only provides an effective solution to the inherent instability issues of traditional network embeddings but invites further inquiry into adversarial frameworks' applicability across varying network models and contexts.