GraphGAN: Graph Representation Learning with Generative Adversarial Nets (1711.08267v1)

Abstract: The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces "fake" samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.

• The paper introduces a novel adversarial framework that combines generative and discriminative models for improved graph embeddings.
• It presents a graph softmax method that leverages graph topology to reduce computational costs in large-scale network tasks.
• GraphGAN achieves up to 11.13% accuracy gains, enhancing key metrics in link prediction and recommendation tasks.

GraphGAN: Graph Representation Learning with Generative Adversarial Nets

The paper "GraphGAN: Graph Representation Learning with Generative Adversarial Nets" by Hongwei Wang et al. presents a novel framework for graph representation learning that combines generative and discriminative models using a generative adversarial network (GAN) approach. Graph representation learning, or network embedding, aims to encode graph vertices into low-dimensional vector spaces to facilitate various analytic tasks like link prediction, node classification, and recommendation.

Framework and Methodology

GraphGAN leverages the synergy between generative and discriminative methods, traditionally separate in graph analytics, by adopting a game-theoretical minimax setup. The key players are:

• Generator: Tries to learn the underlying true connectivity distribution by generating vertices to mimic real connectivity patterns.
• Discriminator: Evaluates the authenticity of the vertex connections made by the generator, attempting to distinguish between actual and generated edges.

The framework iteratively refines both models through adversarial training, enhancing their respective performances. A critical contribution of this paper is the introduction of graph softmax for the generator. Unlike traditional softmax, which inefficiently considers the whole vertex set and ignores graph structure, graph softmax accounts for the topology, offering efficiency and structural awareness.

Experimental Evaluation

The authors evaluate GraphGAN across various realistic datasets, such as arXiv-AstroPh and MovieLens-1M, to test applications like link prediction and recommendation. GraphGAN shows substantial improvements over state-of-the-art methodologies, achieving accuracy gains of up to 11.13% and demonstrating significant enhancements in metrics such as Precision@20 and Recall@20 in recommendation tasks.

Practical Implications and Future Work

GraphGAN's unified adversarial framework effectively captures the intricacies of graph structures, providing robust embeddings applicable across numerous domains. The novel graph softmax significantly reduces computational overhead, making it suitable for large-scale graphs encountered in practical datasets. These aspects make GraphGAN a promising candidate for tasks demanding high dimensionality reduction and prediction accuracy.

Future developments could explore the versatility of GraphGAN with different graph types, such as dynamic or heterogeneous graphs, and investigate its integration with other deep learning paradigms. Further optimization of the graph softmax mechanism might also yield more efficient computational strategies for even larger datasets.

In conclusion, GraphGAN represents a sophisticated approach to graph representation learning, providing a scalable method to improve upon traditional algorithms through adversarial training and innovative generator design.