Learning Robust Representations with Graph Denoising Policy Network Existing representation learning methods based on graph neural networks and their variants rely on the aggregation of neighborhood information, which makes it sensitive to noises in the graph, e.g. erroneous links between nodes, incorrectmissing node features. In this paper, we propose Graph Denoising Policy Network (short for GDPNet) to learn robust representations from noisy graph data through reinforcement learning. GDPNet first selects signal neighborhoods for each node, and then aggregates the information from the selected neighborhoods to learn node representations for the downstream tasks. Specifically, in the signal neighborhood selection phase, GDPNet optimizes the neighborhood for each target node by formulating the process of removing noisy neighborhoods as a Markov decision process and learning a policy with taskspecific rewards received from the representation learning phase. In the representation learning phase, GDPNet aggregates features from signal neighbors to generate node representations for downstream tasks, and provides taskspecific rewards to the signal neighbor selection phase. These two phases are jointly trained to select optimal sets of neighbors for target nodes with maximum cumulative taskspecific rewards, and to learn robust representations for nodes. Experimental results on node classification task demonstrate the effectiveness of GDNet, outperforming the stateoftheart graph representation learning methods on several wellstudied datasets.

gl2vec: learning feature representation using graphlets for directed networks. Learning network representation has a variety  :6,such as network classification. Most existing  :13,this area focuses on static undirected networks and does  :23,for presence of directed edges or temporal  :31,most work focuses on node representations  :38,poorly on tasks like network classification. In :13,this  :47,propose a novel network embedding methodology,  network :54,classification in both static and temporal  :62,gl2vec constructs vectors :23,for feature  static or temporal network :68,graphlet distributions and  :76,model :23,for comparing them against random graphs.  :84,the efficacy and usability of :62,gl2vec over  :92,methods on network :54,classification tasks :6,such  :99,type :54,classification and subgraph identification in  static and temporal directed :106,networks. We argue  :114,provides additional network features that are not  :122,stateoftheart methods, which can significantly improve  :129,accuracy by up to 10% in realworld applications