Adaptive Sampling Towards Fast Graph Representation Learning (1809.05343v3)

Abstract: Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers. In this paper, we accelerate the training of GCNs through developing an adaptive layer-wise sampling method. By constructing the network layer by layer in a top-down passway, we sample the lower layer conditioned on the top one, where the sampled neighborhoods are shared by different parent nodes and the over expansion is avoided owing to the fixed-size sampling. More importantly, the proposed sampler is adaptive and applicable for explicit variance reduction, which in turn enhances the training of our method. Furthermore, we propose a novel and economical approach to promote the message passing over distant nodes by applying skip connections. Intensive experiments on several benchmarks verify the effectiveness of our method regarding the classification accuracy while enjoying faster convergence speed.

Adaptive Sampling Towards Fast Graph Representation Learning

The paper "Adaptive Sampling Towards Fast Graph Representation Learning" addresses the critical challenge of scalability in Graph Convolutional Networks (GCNs) when applied to large-scale graphs. The authors introduce an adaptive layer-wise sampling method aimed at enhancing computational efficiency during GCN training. This approach is notable for reducing the uncontrolled neighborhood expansion across network layers, which traditionally incurs significant computational costs.

Summary of Contributions

1. Layer-wise Sampling Method: The paper pioneers a top-down, layer-wise sampling strategy, contrasting with node-wise techniques. By conditionally sampling nodes in a lower layer based on a fixed-size upper layer, this method cleverly constrains neighborhood expansion, significantly reducing the computational burden while retaining accuracy.
2. Adaptive Sampler: The sampler's adaptability is designed for explicit variance reduction, improving training efficacy. Unlike previous methodologies that depend on fixed or uniform sampling, the adaptive sampler dynamically optimizes towards minimal variance, showing beneficial theoretical implications.
3. Skip Connections for Distant Nodes: The authors propose using skip connections to facilitate communication between distant nodes without extra computations, maintaining second-order proximities efficiently. This novel design bypasses the need for computationally expensive multi-hop sampling.

Experimental Validation

The efficiency and effectiveness of the proposed methods were evaluated across four benchmark datasets: Cora, Citeseer, Pubmed, and Reddit. The results consistently demonstrated improved classification accuracy and faster convergence rates compared to other methods such as GraphSAGE and FastGCN. Notably, the adaptive sampling with explicit variance reduction showed superior performances in terms of stability and computational efficiency.

Implications and Future Directions

The paper's implications are twofold: computational and algorithmic. The proposed sampling strategy and skip connections significantly enhance the computational feasibility of deploying GCNs on large-scale networks by addressing previously prohibitive scalability issues. Algorithmically, the adaptive variance reduction provides a new pathway for designing flexible and efficient graph learning frameworks.

Future research could explore the extension of adaptive sampling to other network architectures beyond GCNs, possibly improving model training on even larger and more complex graph structures. Additionally, deeper investigations into the interactions between sampling strategies and other regularization techniques could yield further advancements in model robustness and performance.

In summary, this paper makes significant strides in overcoming the scalability limitations of GCNs, proposing methods that hold promise for more efficient graph representation learning in practical, large-scale applications.