cube2net: Efficient QuerySpecific Network Construction with Data Cube Organization Networks are widely used to model objects with interactions and have enabled various downstream applications. However, in the real world, network mining is often done on particular query sets of objects, which does not require the construction and computation of networks including all objects in the datasets. In this work, for the first time, we propose to address the problem of queryspecific network construction, to break the efficiency bottlenecks of existing network mining algorithms and facilitate various downstream tasks. To deal with realworld massive networks with complex attributes, we propose to leverage the welldeveloped data cube technology to organize network objects w.r.t their essential attributes. An efficient reinforcement learning algorithm is then developed to automatically explore the data cube structures and construct the optimal queryspecific networks. With extensive experiments of two classic network mining tasks on different realworld large datasets, we show that our proposed cube2net pipeline is general, and much more effective and efficient in queryspecific network construction, compared with other methods without the leverage of data cube or reinforcement learning.

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.