Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention. Though large efforts have been made, they may fail to describe the multiple aspects of similarity between social users, as only a single vector for one unique aspect has been represented for each node. To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. Specifically, we first design a binary mask layer to divide the single vector as conditional embeddings for multiple behaviors. Then, we introduce the attention network to model interaction relationship among multiple preferences, and further utilize the adapted message sending and receiving operation of graph neural network, so that multi-aspect preference information from high-order neighbors will be captured. Finally, we utilize Bayesian Personalized Ranking loss function to learn the preference similarity on each behavior, and jointly learn multiple conditional node embeddings via multi-task learning framework. Extensive experiments on public datasets validate that our MCNE framework could significantly outperform several state-of-the-art baselines, and further support the visualization and transfer learning tasks with excellent interpretability and robustness.

With the recent advances of neural models and natural language processing, automatic generation of classical Chinese poetry has drawn significant attention due to its artistic and cultural value. Previous works mainly focus on generating poetry given keywords or other text information, while visual inspirations for poetry have been rarely explored. Generating poetry from images is much more challenging than generating poetry from text, since images contain very rich visual information which cannot be described completely using several keywords, and a good poem should convey the image accurately. In this paper, we propose a memory based neural model which exploits images to generate poems. Specifically, an Encoder-Decoder model with a topic memory network is proposed to generate classical Chinese poetry from images. To the best of our knowledge, this is the first work attempting to generate classical Chinese poetry from images with neural networks. A comprehensive experimental investigation with both human evaluation and quantitative analysis demonstrates that the proposed model can generate poems which convey images accurately.

Recently, the Network Representation Learning (NRL) techniques, which target at learning the low-dimension vector representation of graph structures, have attracted wide attention due to the effectiveness on various social-oriented application. Though large efforts have been made on the joint analysis combining node attributes with the network structure, they may usually fail to summarize the weighted correlations within nodes and attributes, especially when the nodes suffer extremely sparse attributes. To that end, in this paper, we propose a novel Sparse Attributed Network Embedding (SANE) framework to learn the network structure and sparse attribute information simultaneously in a united approach. Specifically, we first embed the nodes and attributes into a low-dimensional vector space. Then we introduce the pairwise method to capture the interaction between nodes and sparse attributes, and aggregate the attribute information of neighbors to alleviate sparsity for obtaining a better vector representation of node embeddings, which will be used in following network representation learning task. Along this line, we maintain the network structure by maximizing the probability of predicting the center node according to surrounding context nodes. Different from previous work, we introduce an attention mechanism to adaptively weigh the strength of interactions between each context node and the center node, according to the node attribute similarity. Furthermore, we combine the attention network with CBOW model to learn the similarity of the network structure and node attributes simultaneously. Extensive experiments on public datasets have validated the effectiveness of our SANE model with significant margin compared with the state-of-the-art baselines, which demonstrates the potential of adaptively attribute analysis in network embedding.

Patent litigation is an expensive legal process faced by many companies. To reduce the cost of patent litigation, one effective approach is proactive management based on predictive analysis. However, automatic prediction of patent litigation is still an open problem due to the complexity of lawsuits. In this paper, we propose a data-driven framework, Convolutional Tensor Factorization (CTF), to identify the patents that may cause litigations between two companies. Specifically, CTF is a hybrid modeling approach, where the content features from the patents are represented by the Network embedding-combined Convolutional Neural Network (NCNN) and the lawsuit records of companies are summarized in a tensor, respectively. Then, CTF integrates NCNN and tensor factorization to systematically exploit both content information and collaborative information from large amount of data. Finally, the risky patents will be returned by a learning to rank strategy. Extensive experimental results on real-world data demonstrate the effectiveness of our framework.

In this paper, we study the link-oriented tasks in signed network, i.e., labeling link signs and predicting new links. Usually, prior arts directly focus on the link signs, while their intrinsic structural regularities have been largely ignored. Furthermore, these techniques suffer the sensitiveness to the high dimension and sparsity of networks. To deal with these tasks, with verifying the effect of second-order distance in signed network, we propose a novel Link-oriented Signed Network Embedding (LSNE) model, in which network embedding technique is adapted to capture both first-order and second-order distance. Along this line, the link-oriented tasks will be intuitively solved. Extensive experiments on two real-world datasets demonstrate that LSNE could significantly outperform the comparison approaches.