Graph classification is practically important in many domains. To solve this problem, one usually calculates a low-dimensional representation for each node in the graph with supervised or unsupervised approaches. Most existing approaches consider all the edges between nodes while overlooking whether the edge will brings positive or negative influence to the node representation learning. In many real-world applications, however, some connections among the nodes can be noisy for graph convolution, and not all the edges deserve your attention. In this work, we distinguish the positive and negative impacts of the neighbors to the node in graph node classification, and propose to enhance the graph convolutional network by considering the labels between the neighbor edges. We present a novel GCN framework, called Label-aware Graph Convolutional Network (LAGCN), which incorporates the supervised and unsupervised learning by introducing the edge label predictor. As a general model, LAGCN can be easily adapted in various previous GCN and enhance their performance with some theoretical guarantees. Experimental results on multiple real-world datasets show that LAGCN is competitive against various state-of-the-art methods in graph classification.
Another class of methods can enhance the GCN network are using the attention mechanism, GAT @cite_35 first applies the idea of self-attention to graph representation learning. GAT gives different weights to the neighbors of the center node by weighting the similarity between each neighbor and the center node. However, the computation of attention consumes a large amount of time. Gaan @cite_37 accelerate the training and prediction of GAT by using the node-wise sampling method mentioned in GraphSAGE. ASGCN @cite_19 @cite_29 ensemble the node-wise sampling, layer-wise sampling and the attention mechanism to achieve a better performance. However, these methods are not aware of the labels of the edges, which may aggregate the information from the neighbors whose labels are different. For nodes which have fewer neighbors than others, these methods also suffer from lacking of localize information.
@cite_19 @cite_35 @cite_37 @cite_29
2890703109 2789186812 2963695795 2891894830
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. Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCN model improves state-of-the-art baselines on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations. The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. Such a model, however, is transductive in nature because parameters are learned through convolutions with both training and test data. Moreover, the recursive neighborhood expansion across layers poses time and memory challenges for training with large, dense graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions as integral transforms of embedding functions under probability measures. Such an interpretation allows for the use of Monte Carlo approaches to consistently estimate the integrals, which in turn leads to a batched training scheme as we propose in this work---FastGCN. Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference. We show a comprehensive set of experiments to demonstrate its effectiveness compared with GCN and related models. In particular, training is orders of magnitude more efficient while predictions remain comparably accurate. The field of object detection has made great progress in recent years. Most of these improvements are derived from using a more sophisticated convolutional neural network. However, in the case of humans, the attention mechanism, global structure information, and local details of objects all play an important role for detecting an object. In this paper, we propose a novel fully convolutional network, named as Attention CoupleNet, to incorporate the attention-related information and global and local information of objects to improve the detection performance. Specifically, we first design a cascade attention structure to perceive the global scene of the image and generate class-agnostic attention maps. Then the attention maps are encoded into the network to acquire object-aware features. Next, we propose a unique fully convolutional coupling structure to couple global structure and local parts of the object to further formulate a discriminative feature representation. To fully explore the global and local properties, we also design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local information. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging data sets, i.e., a mAP of 85.7 on VOC07, 84.3 on VOC12, and 35.4 on COCO. Codes are publicly available at https: github.com tshizys CoupleNet .