Collaborative Graph Walk for SemiSupervised Multilabel Node Classification In this work, we study semisupervised multilabel node classification problem in attributed graphs. Classic solutions to multilabel 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 MultiLabelGraphWalk, to finely tune node representations with the available label assignments in attributed graphs via reinforcement learning. The proposed method formulates the multilabel node classification task as simultaneous graph walks conducted by multiple labelspecific agents. Furthermore, policies of the labelwise 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 labelspecific classification tasks. A comprehensive experimental study demonstrates that the proposed method can achieve significantly better multilabel classification performance than the stateoftheart approaches and conduct more efficient graph exploration.

RiWalk: Fast Structural Node Embedding via Role Identification Nodes performing different functions in a network have different roles, and these roles can be gleaned from the structure of the network. Learning latent representations for the roles of nodes helps to understand the network and to transfer knowledge across networks. However, most existing structural embedding approaches suffer from high computation and space cost or rely on heuristic feature engineering. Here we propose RiWalk, a flexible paradigm for learning structural node representations as it decouples the structural embedding problem into a role identification procedure and a network embedding procedure. Through role identification, rooted kernels with structural dependencies kept are built to better integrate network embedding methods. To demonstrate the effectiveness of RiWalk, we develop two different role identification methods named RiWalkSP and RiWalkWL respectively and employ random walk based network embedding methods. Experiments on withinnetwork classification tasks show that our proposed algorithms achieve comparable performance with other baselines while being an order of magnitude more efficient. Besides, we also conduct acrossnetwork role classification tasks. The results show potential of structural embeddings in transfer learning. RiWalk is also scalable, making it capable of capturing structural roles in massive networks.