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  1. Network alignment is a critical steppingstone behind a variety of multi-network mining tasks. Most of the existing methods essentially optimize a Frobenius-like distance or ranking-based loss, ignoring the underlying geometry of graph data. Optimal transport (OT), together with Wasserstein distance, has emerged to be a powerful approach accounting for the underlying geometry explicitly. Promising as it might be, the state-of-the-art OT-based alignment methods suffer from two fundamental limitations, including (1) effectiveness due to the insufficient use of topology and consistency information and (2) scalability due to the non-convex formulation and repeated computationally costly loss calculation. In this paper, we propose a position-aware regularized optimal transport framework for network alignment named PARROT. To tackle the effectiveness issue, the proposed PARROT captures topology information by random walk with restart, with three carefully designed consistency regularization terms. To tackle the scalability issue, the regularized OT problem is decomposed into a series of convex subproblems and can be efficiently solved by the proposed constrained proximal point method with guaranteed convergence. Extensive experiments show that our algorithm achieves significant improvements in both effectiveness and scalability, outperforming the state-of-the-art network alignment methods and speeding up existing OT-based methods by up to 100 times. 
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    Free, publicly-accessible full text available April 30, 2024
  2. In a connected world, fair graph learning is becoming increasingly important because of the growing concerns about bias. Yet, the vast majority of existing works assume that the input graph comes from a single view while ignoring the multi-view essence of graphs. Generally speaking, the bias in graph mining is often rooted in the input graph and is further introduced or even amplified by the graph mining model. It thus poses critical research questions regarding the intrinsic relationships of fairness on different views and the possibility of mitigating bias on multiple views simultaneously. To answer these questions, in this paper, we explore individual fairness in multi-view graph mining. We first demonstrate the necessity of fair multi-view graph learning. Building upon the optimization perspective of fair single-view graph mining, we then formulate our problem as a linear weighted optimization problem. In order to figure out the weight of each view, we resort to the minimax Pareto fairness, which is closely related to the Rawlsian difference principle, and propose an effective solver named iFiG that minimizes the utility loss while promoting individual fairness for each view with two different instantiations. The extensive experiments that we conduct in the application of multi-view spectral clustering and INFORM post-processing demonstrate the efficacy of our proposed method in individual bias mitigation. 
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  3. Recent years have witnessed the superior performance of heterogeneous graph neural networks (HGNNs) in dealing with heterogeneous information networks (HINs). Nonetheless, the success of HGNNs often depends on the availability of sufficient labeled training data, which can be very expensive to obtain in real scenarios. Active learning provides an effective solution to tackle the data scarcity challenge. For the vast majority of the existing work regarding active learning on graphs, they mainly focus on homogeneous graphs, and thus fall in short or even become inapplicable on HINs. In this paper, we study the active learning problem with HGNNs and propose a novel meta-reinforced active learning framework MetRA. Previous reinforced active learning algorithms train the policy network on labeled source graphs and directly transfer the policy to the target graph without any adaptation. To better exploit the information from the target graph in the adaptation phase, we propose a novel policy transfer algorithm based on meta-Q-learning termed per-step MQL. Empirical evaluations on HINs demonstrate the effectiveness of our proposed framework. The improvement over the best baseline is up to 7% in Micro-F1. 
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  4. Knowledge graph reasoning plays a pivotal role in many real-world applications, such as network alignment, computational fact-checking, recommendation, and many more. Among these applications, knowledge graph completion (KGC) and multi-hop question answering over knowledge graph (Multi-hop KGQA) are two representative reasoning tasks. In the vast majority of the existing works, the two tasks are considered separately with different models or algorithms. However, we envision that KGC and Multi-hop KGQA are closely related to each other. Therefore, the two tasks will benefit from each other if they are approached adequately. In this work, we propose a neural model named BiNet to jointly handle KGC and multi-hop KGQA, and formulate it as a multi-task learning problem. Specifically, our proposed model leverages a shared embedding space and an answer scoring module, which allows the two tasks to automatically share latent features and learn the interactions between natural language question decoder and answer scoring module. Compared to the existing methods, the proposed BiNet model addresses both multi-hop KGQA and KGC tasks simultaneously with superior performance. Experiment results show that BiNet outperforms state-of-the-art methods on a wide range of KGQA and KGC benchmark datasets. 
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  5. Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications. Despite the successes of GCN deployment, GCN often exhibits performance disparity with respect to node de- grees, resulting in worse predictive accuracy for low-degree nodes. We formulate the problem of mitigating the degree-related per- formance disparity in GCN from the perspective of the Rawlsian difference principle, which is originated from the theory of distribu- tive justice. Mathematically, we aim to balance the utility between low-degree nodes and high-degree nodes while minimizing the task- specific loss. Specifically, we reveal the root cause of this degree- related unfairness by analyzing the gradients of weight matrices in GCN. Guided by the gradients of weight matrices, we further propose a pre-processing method RawlsGCN-Graph and an in- processing method RawlsGCN-Grad that achieves fair predictive accuracy in low-degree nodes without modification on the GCN architecture or introduction of additional parameters. Extensive experiments on real-world graphs demonstrate the effectiveness of our proposed RawlsGCN methods in significantly reducing degree- related bias while retaining comparable overall performance. 
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  6. Dense subgraph detection is a fundamental building block for a va- riety of applications. Most of the existing methods aim to discover dense subgraphs within either a single network or a multi-view network while ignoring the informative node dependencies across multiple layers of networks in a complex system. To date, it largely remains a daunting task to detect dense subgraphs on multi-layered networks. In this paper, we formulate the problem of dense sub- graph detection on multi-layered networks based on cross-layer consistency principle. We further propose a novel algorithm Des- tine based on projected gradient descent with the following ad- vantages. First, armed with the cross-layer dependencies, Destine is able to detect significantly more accurate and meaningful dense subgraphs at each layer. Second, it scales linearly w.r.t. the num- ber of links in the multi-layered network. Extensive experiments demonstrate the efficacy of the proposed Destine algorithm in various cases. 
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    Network alignment plays an important role in a variety of applications. Many traditional methods explicitly or implicitly assume the alignment consistency which might suffer from over-smoothness, whereas some recent embedding based methods could somewhat embrace the alignment disparity by sampling negative alignment pairs. However, under different or even competing designs of negative sampling distributions, some methods advocate positive correlation which could result in false negative samples incorrectly violating the alignment consistency, whereas others champion negative correlation or uniform distribution to sample nodes which may contribute little to learning meaningful embeddings. In this paper, we demystify the intrinsic relationships behind various network alignment methods and between these competing design principles of sampling. Specifically, in terms of model design, we theoretically reveal the close connections between a special graph convolutional network model and the traditional consistency based alignment method. For model training, we quantify the risk of embedding learning for network alignment with respect to the sampling distributions. Based on these, we propose NeXtAlign which strikes a balance between alignment consistency and disparity. We conduct extensive experiments that demonstrate the proposed method achieves significant improvements over the state-of-the-arts. 
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