Graph contrastive learning (GCL) has attracted rising research attention recently due to its effectiveness in self- supervised graph learning. A key step of GCL is to conduct data augmentation, based on which self-supervised learning is performed through the contrast between two augmented data views. Existing approaches generally generate the two data views from the original graph, which has been revealed to be less effective due to the lack of data diversity. Meanwhile, although the data augmentation methods and the contrastive modes have been extensively studied, the effect of hard negative samples (i.e.samples that are difficult to distinguish from an anchor node) on GCL is not fully explored. In this paper, we propose a novel complementary graph contrastive learning method boosted by adversarial hard negative sample generation. Specifically, we first construct a κNN graph as the complementary counterpart of the original graph in the semantic space. Then graph augmentation is conducted in both the semantic and topology spaces for the two complementary graphs to obtain two contrastive views with a larger data diversity. To facilitate the contrastive learning, an adversarial network named ADNet is also proposed to generate hard negative samples. The generated samples are more informative and challenging, and thus can further boost the learning performance. Extensive evaluations over the node classification task demonstrate that our proposal outperforms existing state-of-the-art GCL methods, and even exceeds supervised approaches. The code of this work is publicly available at https://github.com/sktsherlock/HNGCL-V1.

Urban flow imputation, which aims to infer the missing flows of some locations based on the available flows of surrounding areas, is critically important to various smart city related applications such as urban planning and public safety. Although many methods are proposed to impute time series data, they may not be feasible to be directly applied on urban flow data due to the following reasons. First, urban flows have the complex spatial and temporal correlations which are much harder to be captured compared with time series data. Second, the urban flow data can be random missing (i.e., missing randomly in terms of times and locations) or block missing (i.e., missing for all locations in a particular time slot). Thus it is difficult for existing methods to work well on both scenarios. In this paper, we for the first time study the urban flow imputation problem and propose a generative-free Attention-based Spatial-Temporal Combine and Mix Completion Network model (AST-CMCN for short) to effectively address it. Specifically, AST-CMCN consists of a Spatial and Temporal Completion Network (SATCNet for short) and a Spatial-Temporal Mix Completion Network (STMCNet for short). SATCNet is composed of stacked GRUAtt modules to capture the geographical and temporal correlations of the urban flows, separately. STMCNet is designed to capture the complex spatial-temporal associations jointly between historical urban flows and current data. A Message Passing module is also proposed to capture new spatial-temporal patterns that never appear in the historical data. Extensive experiments on two large real-world datasets validate the effectiveness and efficiency of our method compared with the state-of-the-art baselines.

Sea Surface Temperature (SST) prediction has attracted increasing attention due to its critical role in climate change. Traditional SST prediction methods can be mainly divided into two types, the physics-based numerical methods and the data-driven methods. However, the above methods have certain limitations, the former type can not perform well when the physical prior information is incomplete, while latter type can not perform well when the training data is insufficient. This paper uses a deep neural network to extract some valuable information from the data, and then introduces the space-time partial differential equation (PDE) to model the prior physical information referring to SST. By incorporating them together, a new Space-Time PDE-guided Neural Network (STPDE-NET), which can better deal with the prior physical information incompleteness and data insufficiency problems mentioned above is proposed. In the experiments, we compare our STPDE-NET with several famous or state-of-the-art SST prediction methods. The experimental results show that STPDE-NET outperforms the compared methods in most SST prediction circumstances, especially when the training data is insufficient.

Anchor links connect information entities, such as entities of movies or products, across networks from different sources, and thus information in these networks can be transferred directly via anchor links. Therefore, anchor links have great value to many cross-network applications, such as cross-network social link prediction and cross-network recommendation. In this paper, we focus on studying the recommendation problem that can provide ratings of items or services. To address the problem, we propose a Cross-network Collaborative Matrix Factorization (CCMF) recommendation framework based on broad learning setting, which can effectively integrate multi-source information and alleviate the sparse information problem in each individual network. Based on item anchor links CCMF can fuse item similarity information and item latent information across networks from different sources. And different from most of the traditional works, CCMF can make multi-source recommendation tasks collaborate together via the information transfer based on the broad learning setting. During the transfer process, a novel cross-network similarity transfer method is applied to keep the consistency of item similarities between two different networks, and a domain adaptation matrix is used to overcome the domain difference problem. We conduct experiments to compare the proposed CCMF method with both classic and state-of-the-art recommendation techniques. The experimental results illustrate that CCMF outperforms other methods in different experimental circumstances, and has great advantages on dealing with different data sparse problems.

Inferring the links among entities in networks is an important research problem for various disciplines. Depending on the specific application settings, the links to be inferred are usually subject to different cardinality constraints, like one-to-one, one-to-many and many-to-many. However, most existing research works on link prediction problems fail to consider such a kind of constraint. In this paper, we propose to study the link prediction problem with general cardinality constraints, which is formally defined as the CLP (Cardinality Constrained Link Prediction) problem. By minimizing the projection loss of links from feature vectors to labels, the CLP problem is formulated as an optimization problem involving multiple variables, where the cardinality constraints are modeled as mathematical constraints on node degrees. The objective function is shown to be not jointly convex and the optimal solution subject to the cardinality constraints can be very time-consuming to achieve. To solve the optimization problem, an iterative variable updating based link prediction framework ITERCLIPS (Iterative Constrained Link Prediction & Selection) is introduced in this paper, which involves the steps on link updating and selection alternatively. To overcome the high time cost problem, a greedy link selection step is introduced in this paper, which picks links greedily while preserving the link cardinality constraints simultaneously. Meanwhile, to ensure the effectiveness of ITERCLIPS on large-scale networks, a distributed implementation of ITERCLIPS is further presented as a scalable solution to the CLP problem. Extensive experiments have been done on three real-world network datasets with different types of cardinality constraints, and the experimental results achieved by ITERCLIPS on all these datasets can demonstrate the effectiveness and advantages of ITERCLIPS in solving the CLP problem.