The task of searching over large keyword graphs aims to identify a subgraph where the nodes collectively cover the input query keywords. Although finding an exact solution to this problem is NP-hard, we address it by ...
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The task of searching over large keyword graphs aims to identify a subgraph where the nodes collectively cover the input query keywords. Although finding an exact solution to this problem is NP-hard, we address it by proposing a novel graph neural network representationlearning technique specifically tailored for graphs with missing information. We propose a novel keyword graph representation learning method that incorporates complementary aspects of graphs: global, local, adjusted, and feature semantics. Considering these multiple aspects, our approach remains robust and resilient to missing information. We adopt and fine-tune a transformer-based model to aggregate the various features of a graph to generate rich representations, recognizing the pivotal role of keywords in this task. We show through experiments on real-world data that our method outperforms the state-of-the-art approaches and is particularly robust in the face of missing values, underscoring its ability to effectively handle incomplete graphs.
graph Transformers have garnered significant attention due to their ability to address the challenges of long-distance interactions in previous GNNs. However, most current graph Transformers face difficulties when dea...
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graph Transformers have garnered significant attention due to their ability to address the challenges of long-distance interactions in previous GNNs. However, most current graph Transformers face difficulties when dealing with heterophilic graphs. To investigate this issue, we first analyzed the distribution of attention weights for homophilic and heterophilic graphs. We discovered that heterophily interferes with the allocation of attention weights, leading to errors in node classification. Further investigation revealed that the root cause may be the difficulty of current graph Transformers in capturing the difference between the features of each node and its neighbors. To alleviate this issue, we propose a position encoding strategy called DiSP to better capture the feature difference, and introduce FDphormer, a new efficient and simple graph Transformer model based on DiSP. Additionally, we analyze the generalization error of existing graph Transformer models and provide an upper bound on the generalization error of current graph Transformers with the introduction of DiSP. Extensive experiments demonstrate that FDphormer not only outperforms state-of-the-art methods on diverse heterogeneous datasets but also exhibits competitive performance under homophily.
In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has a...
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In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques. Specifically, this survey first provides an overview of various feasible taxonomies and categorizes existing GDAug studies based on multi-scale graph elements. Subsequently, for each type of GDAug technique, this survey formalizes standardized technical definition, discuss the technical details, and provide schematic illustration. The survey also reviews domain-specific graph data augmentation techniques, including those for heterogeneous graphs, temporal graphs, spatio-temporal graphs, and hypergraphs. In addition, this survey provides a summary of available evaluation metrics and design guidelines for graph data augmentation. Lastly, it outlines the applications of GDAug at both the data and model levels, discusses open issues in the field, and looks forward to future directions. The latest advances in GDAug are summarized in GitHub.
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