Transfer Learning across Networks for Collective Classification

This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201

Search Efficient Binary Network Embedding

Traditional network embedding primarily focuses on learning a dense vector representation for each node, which encodes network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned dense vector representations are inefficient for large-scale similarity search, which requires to find the nearest neighbor measured by Euclidean distance in a continuous vector space. In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a sparse binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations efficiently through a stochastic gradient descent based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support much quicker network node search compared to Euclidean distance or other distance measures. Our experiments and comparisons show that BinaryNE not only delivers more than 23 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods

Uncovering dynamically critical regions in near-wall turbulence using 3D Convolutional Neural Networks

Near-wall regions in wall-bounded turbulent flows experience strong intermittent events involving ejections of slow-moving fluid parcels away from the wall, and `sweeps' of faster moving fluid towards the wall. Here, we train a three-dimensional Convolutional Neural Network (CNN) to predict the intensity of ejection events that occur in Direct Numerical Simulation (DNS) of a periodic channel flow. The trained network is able to predict burst intensities accurately for flow snaphshots that are sufficiently removed from the training data so as to be temporally decorrelated. More importantly, we probe the trained network to reveal regions of the flow where the network focuses its attention in order to make a prediction. We find that these salient regions correlate very well with fluid parcels being ejected away from the wall. Moreover, the CNN is able to keep track of the salient fluid parcels as the flow evolves in time. This demonstrates that CNNs are capable of discovering dynamically critical phenomena in turbulent flows without requiring any a-priori knowledge of the underlying dynamics. Remarkably, the trained CNN is able to predict ejection intensities accurately for data at different Reynolds numbers, which highlights its ability to identify physical processes that persist across varying flow conditions. The results presented here highlight the immense potential of CNNs for discovering and analyzing nonlinear spatial correlations in turbulent flows.Comment: 10 pages, 7 figure