Query: NODE CLASSIFICATION

title: Meta GNN: On Few shot Node Classification in Graph Meta learning
abstract: Meta learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i.e. acquiring new knowledge and skills with little or even no demonstration. Most of the existing meta learning methods are proposed to tackle few shot learning problems such as image and text, in rather Euclidean domain. However, there are very few works applying meta learning to non Euclidean domains, and the recently proposed graph neural networks (GNNs) models do not perform effectively on graph few shot learning problems. Towards this, we propose a novel graph meta learning framework Meta GNN to tackle the few shot node classification problem in graph meta learning settings. It obtains the prior knowledge of classifiers by training on many similar few shot learning tasks and then classifies the nodes from new classes with only few labeled samples. Additionally, Meta GNN is a general model that can be straightforwardly incorporated into any existing state of the art GNN. Our experiments conducted on three benchmark datasets demonstrate that our proposed approach not only improves the node classification performance by a large margin on few shot learning problems in meta learning paradigm, but also learns a more general and flexible model for task adaption.

title: Adaptive Neural Network for Node Classification in Dynamic Networks
abstract: Given a network with the labels for a subset of nodes, transductive node classification targets to predict the labels for the remaining nodes in the network. This technique has been used in a variety of applications such as voxel functionality detection in brain network and group label prediction in social network. Most existing node classification approaches are performed in static networks. However, many real world networks are dynamic and evolve over time. The dynamics of both node attributes and network topology jointly determine the node labels. In this paper, we study the problem of classifying the nodes in dynamic networks. The task is challenging for three reasons. First, it is hard to effectively learn the spatial and temporal information simultaneously. Second, the network evolution is complex. The evolving patterns lie in both node attributes and network topology. Third, for different networks or even different nodes in the same network, the node attributes, the neighborhood node representations and the network topology usually affect the node labels differently, it is desirable to assess the relative importance of different factors over evolutionary time scales. To address the challenges, we propose AdaNN, an adaptive neural network for transductive node classification. AdaNN learns node attribute information by aggregating the node and its neighbors, and extracts network topology information with a random walk strategy. The attribute information and topology information are further fed into two connected gated recurrent units to learn the spatio temporal contextual information. Additionally, a triple attention module is designed to automatically model the different factors that influence the node representations. AdaNN is the first node classification model that is adaptive to different kinds of dynamic networks. Extensive experiments on real datasets demonstrate the effectiveness of AdaNN.

title: Learning deep neural networks for node classification
abstract: Deep Neural Network (DNN) has made great leaps in image classification and speech recognition in recent years. However, employing DNN for node classification such as in social network remains to be a non trivial problem. Moreover, the current advanced method of implementing node classification tasks usually takes two steps, i.e. firstly, the embedding vector of the node is obtained through network embedding and then the classifier such as SVM is leveraged to do the task. Distinctly, this may only get the suboptimal solution of the problem. To settle the above issues, a novel Deep Neural Network method for node classification named DNNNC is proposed in the framework of Deep Learning. Specifically, we first get the positive pointwise mutual information (PPMI) matrix from the given adjacency matrix. Then, the data is fed to deep neural network composed of deep stacked sparse autoencoders and softmax layer, which could learn the node representation while encoding the rich nonlinear structural and semantic information and could be well trained for node classification under the DNN framework. Extensive experiments are conducted on real world network datasets for node classification task and have shown that the proposed model DNNNC outperforms the state of the art method in the view of superior performance.

title: Network Embedding for Cross network Node Classification
abstract: Network embedding is an effective method to learn low dimensional node vector representations which can well preserve the original network structures. Existing network embedding algorithms are mostly developed for a single network, which fail to learn generalized and comparable feature representations across networks. In this paper, we address a cross network node classification problem by leveraging the abundant labeled information from a source network to help classify unlabeled nodes in a target network. A cross network deep network embedding (CDNE) model is proposed to embed the nodes from the source network and the target network into a unified low dimensional latent space. This model integrates deep network embedding and domain adaptation to learn label discriminative and network invariant node vector representations. The network structures, node attributes and node labels are leveraged collectively to learn similar hidden vector representations for similar nodes within a network and across different networks. Extensive experimental results demonstrate that the proposed model significantly outperforms the state of the art related algorithms for node classification in the target network.

title: Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks
abstract: Node classification is an important problem in relational machine learning. However, in scenarios where graph edges represent interactions among the entities (e.g. over time) the majority of current methods either summarize the interaction information into link weights or aggregate the links to produce a static graph. In this paper, we propose a neural network architecture that jointly captures both temporal and static interaction patterns, which we call Temporal Static Graph Net (TSGNet) Our key insight is that leveraging both a static neighbor encoder, which can learn aggregate neighbor patterns, and a graph neural network based recurrent unit, which can capture complex interaction patterns, improve the performance of node classification. In our experiments on node classification tasks, TSGNet produces significant gains compared to state of the art methods reducing classification error up to 24% and an average of 10% compared to the best competitor on four real world networks and one synthetic dataset.

title: Node Classification for Signed Social Networks Using Diffuse Interface Methods
abstract: Signed networks contain both positive and negative kinds of interactions like friendship and enmity. The task of node classification in non signed graphs has proven to be beneficial in many real world applications, yet extensions to signed networks remain largely unexplored. In this paper we introduce the first analysis of node classification in signed social networks via diffuse interface methods based on the Ginzburg Landau functional together with different extensions of the graph Laplacian to signed networks. We show that blending the information from both positive and negative interactions leads to performance improvement in real signed social networks, consistently outperforming the current state of the art.

title: Hashing Graph Convolution for Node Classification
abstract: Convolution on graphs has aroused great interest in AI due to its potential applications to non gridded data. To bypass the influence of ordering and different node degrees, the summation/average diffusion/aggregation is often imposed on local receptive field in most prior works. However, the collapsing into one node in this way tends to cause signal entanglements of nodes, which would result in a sub optimal feature and decrease the discriminability of nodes. To address this problem, in this paper, we propose a simple but effective Hashing Graph Convolution (HGC) method by using global hashing and local projection on node aggregation for the task of node classification. In contrast to the conventional aggregation with a full collision, the hash projection can greatly reduce the collision probability during gathering neighbor nodes. Another incidental effect of hash projection is that the receptive field of each node is normalized into a common size bucket space, which not only staves off the trouble of different size neighbors and their order but also makes a graph convolution run like the standard shape gridded convolution. Considering the few training samples, also, we introduce a prediction consistent regularization term into HGC to constrain the score consistency of unlabeled nodes in the graph. HGC is evaluated on both transductive and inductive experimental settings and achieves new state of the art results on all datasets for node classification task. The extensive experiments demonstrate the effectiveness of hash projection.

title: Collaborative Graph Walk for Semi Supervised Multi label Node Classification
abstract: In this work, we study semi supervised multi label node classification problem in attributed graphs. Classic solutions to multi label node classification follow two steps, first learn node embedding and then build a node classifier on the learned embedding. To improve the discriminating power of the node embedding, we propose a novel collaborative graph walk, named Multi Label Graph Walk, to finely tune node representations with the available label assignments in attributed graphs via reinforcement learning. The proposed method formulates the multi label node classification task as simultaneous graph walks conducted by multiple label specific agents. Furthermore, policies of the label wise graph walks are learned in a cooperative way to capture first the predictive relation between node labels and structural attributes of graphs; and second, the correlation among the multiple label specific classification tasks. A comprehensive experimental study demonstrates that the proposed method can achieve significantly better multi label classification performance than the state of the art approaches and conduct more efficient graph exploration.

title: Neighborhood Adaptive Graph Convolutional Network for Node Classification
abstract: Recently, graph convolutional neural network as an efficient and effective method has experienced significant attention and becomes the de facto method for learning node or graph representations. However, existing most methods use a fixed order neighborhood information when integrating node representations for node classification on the graph. In this paper, we present a neighborhood adaptive graph convolutional network (NAGCN) a novel method to efficiently learn each node's representations. Particularly, we construct a convolutional kernel abstracted from the diffusion process, named as the neighborhood adaptive kernel to more precisely learn and integrate related neighborhood node information for each node. As a result, our proposed method can learn more useful information across the relevant near and distant neighbors according to the real applications. We also adopt a threshold mechanism on the constructed kernel to better reserve the most impact neighbor vertices for each node on the graph. Besides, one learnable feature refinement process is used in the model to obtain high level node representations with sufficient expressive power. The model is also theoretically analyzed in terms of spectral convolution and message passing algorithm. Notably, extensive experiments demonstrate that our method can achieve better performance on node classification tasks compared to other related approaches.

title: Node classification framework
abstract: GCN is a recent effective algorithm which effectively learns a function incorporate both graph structure and node features for semisupervised graph based node classification. Although GCN exceeds other state of the art methods, the number of parameters that need to be learned are still significantly more than the number of used samples. Because only labeled nodes and their 2 hop neighbors are used for learning parameters in GCN. As a consequence the accuracy of node classification declines sharply when decreasing the size of train data. In order to reducing parameters and making better use of unlabeled nodes, we proposes to use unsupervised representation algorithm such as autoencoder to pretrain a embedding for each node, which captures effective information from input features and reduce parameters in graph convolution layers. Experiments show that our model has increased by 0.87% 1.96% 1.87% compared to baseline on Cora, Citeseer and Pubmed data respectively. The performance of our model declines slower than GCN when decreasing the train samples.