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Graph Representation Learning via Aggregation Enhancement
Graph Representation Learning via Aggregation Enhancement. Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and propagating information between layers, which limits their performance. We tackle this problem with the kernel regression (KR) approach ...
Graph Representation Learning via Aggregation Enhancement
Graph Representation Learning via Aggregation Enhancement KR KR + KR GIRL (a) (b) KR loss Kernel Regression Self-Supervised Loss Input graph GNN Representations Figure 1. (a) A schematic representation of GIRL algorithm, where L SSLis the sum of KR losses between G inand G outof each GNN layer g '. (b) A schematic representation of KR.
Graph Representation Learning via Aggregation Enhancement
This work highlights the potential of KR to advance the field of graph representation learning and enhance the performance of GNNs, using KR loss as the primary loss in self-supervised settings or as a regularization term in supervised settings. Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and ...
Graph Representation Learning
In the era of statistical learning, such symbolic representations are widely used in graph-based NLP such as TextRank [], where word and sentence graphs can be respectively built for keyword extraction [] and extractive document summarization [].Although convenient and straightforward, the representation of the adjacency matrix suffers from the scalability problem.
Learning graph representation by aggregating subgraphs via mutual
Especially in the Subgraph-Agg stage, to obtain meaningful graph representations from the view of the subgraphs, we introduce an auto-regressive method, a universal self-supervised framework for the graph generation. The overall pipeline of proposed method can be seen in Fig. 2. 3.1. Preliminary. Unsupervised Learning on Graphs.
Graph representation learning via simple jumping knowledge networks
Recent graph neural networks for graph representation learning depend on a neighborhood aggregation process. Several works focus on simplifying the neighborhood aggregation process and model structures. However, as the depth of the models increases, the simplified models will encounter oversmoothing, resulting in a decrease in model performance. Several works leverage sophisticated learnable ...
Learning Graph Representation by Aggregating Subgraphs via Mutual
In this paper, we introduce a self-supervised learning method to enhance the graph-level representations with the help of a set of subgraphs. For this purpose, we propose a universal framework to generate subgraphs in an auto-regressive way and then using these subgraphs to guide the learning of graph representation by Graph Neural Networks. Under this framework, we can get a comprehensive ...
Learning graph representation by aggregating subgraphs via mutual
And GRACE [33] introduces a framework for unsupervised graph representation learning by generating two graph views and maximizing the agreement of node representations in these two views. There are also pre-train methods [34] of using contrastive learning, which can achieve excellent results on various graph-level downstream tasks with few labels.
We study the graph representation learning problem that has emerged with the advent of numerous graph analysis tasks in the recent past. The task of representation learning from graphs of heterogeneous object attributes and complex topological structures is important yet challenging in practice. We propose an Attribute-interactive Neighborhood-aggregative Graph learning scheme (ANGraph), which ...
Enhancing Graph Representations Learning with Decorrelated Propagation
In recent years, graph neural networks (GNNs) have been widely used in many domains due to their powerful capability in representation learning on graph-structured data. While a majority of extant studies focus on mitigating the over-smoothing problem, recent works also reveal the limitation of GNN from a new over-correlation perspective which ...
Efficient and Effective Edge-wise Graph Representation Learning
Graph representation learning (GRL) is a powerful tool for graph analysis, which has gained massive attention from both academia and industry due to its superior performance in various real-world applications. However, the majority of existing works for GRL are dedicated to node-based tasks and thus focus on producing node representations.
Tree Structure-Aware Graph Representation Learning via Integrated
While Graph Neural Network (GNN) has shown superiority in learning node representations of homogeneous graphs, leveraging GNN on heterogeneous graphs remains a challenging problem. The dominating reason is that GNN learns node representations by aggregating neighbors' information regardless of node types. Some work is proposed to alleviate such issue by exploiting relations or meta-path to ...
Graph Representation Ensemble Learning
Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links and classifying and recommending nodes. Most embedding methods aim to preserve specific properties of the original graph in the low dimensional space. However, real-world graphs have a combination of several features that are difficult to characterize and capture by a single ...
ERGCN: Data enhancement-based robust graph convolutional network
1. Introduction. Graphs are powerful data representations that can model numerous complex systems across various areas such as social media [1], knowledge graphs [2], e-commerce transactions [3], and many other research areas [4].Because of the expressive power of graphs, a massive amount of graph-structured data has been accumulated, and graph machine learning (GML) has attracted considerable ...
Enhanced Graph Representations for Graph Convolutional ...
Graph Convolutional Network (GCN) is increasingly becoming popular among researchers for its capability of solving the task of classification of nodes, graphs or links. Graphs being a very useful representation for several application domains are increasingly grabbing the attention of researchers. Methods are being proposed to extract meaningful information in a form which can be used by ...
PDF Learning Representation over Dynamic Graph using Aggregation-Diffusion
such as bioinformatics, knowledge graphs, and social networks. The propagation of information in graphs is important in learning dynamic graph representations, and most of the existing methods achieve this by aggregation. However, relying only on aggregation to propagate information in dynamic graphs can result in delays in information ...
Multi-view contrastive clustering via integrating graph aggregation and
In this paper, we propose a novel end-to-end MVC method called Multi-view contrAstive clustering with Integrated Graph Aggregation and confidence enhance (MAGA).MAGA simultaneously performs multi-view information aggregation and contrast, as illustrated in Fig. 1.Here's a detailed overview of our approach: We begin by learning a latent representation for each view using view-specific ...
Large-Scale Representation Learning on Graphs via Bootstrapping
Bootstrapped Graph Latents (BGRL) is introduced - a graph representation learning method that learns by predicting alternative augmentations of the input and is thus scalable by design, achieving state-of-the-art performance and improving over supervised baselines where representations are shaped only through label information. Expand.
HHSE: heterogeneous graph neural network via higher-order semantic
Heterogeneous graph representation learning has strong expressiveness when dealing with large-scale relational graph data, and its purpose is to effectively represent the semantic information and heterogeneous structure information of nodes in the graph. Current methods typically use shallow models to embed semantic information on low-order neighbor nodes in the graph, which prevents the ...
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COMMENTS
Graph Representation Learning via Aggregation Enhancement. Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and propagating information between layers, which limits their performance. We tackle this problem with the kernel regression (KR) approach ...
Graph Representation Learning via Aggregation Enhancement KR KR + KR GIRL (a) (b) KR loss Kernel Regression Self-Supervised Loss Input graph GNN Representations Figure 1. (a) A schematic representation of GIRL algorithm, where L SSLis the sum of KR losses between G inand G outof each GNN layer g '. (b) A schematic representation of KR.
This work highlights the potential of KR to advance the field of graph representation learning and enhance the performance of GNNs, using KR loss as the primary loss in self-supervised settings or as a regularization term in supervised settings. Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and ...
In the era of statistical learning, such symbolic representations are widely used in graph-based NLP such as TextRank [], where word and sentence graphs can be respectively built for keyword extraction [] and extractive document summarization [].Although convenient and straightforward, the representation of the adjacency matrix suffers from the scalability problem.
Especially in the Subgraph-Agg stage, to obtain meaningful graph representations from the view of the subgraphs, we introduce an auto-regressive method, a universal self-supervised framework for the graph generation. The overall pipeline of proposed method can be seen in Fig. 2. 3.1. Preliminary. Unsupervised Learning on Graphs.
Recent graph neural networks for graph representation learning depend on a neighborhood aggregation process. Several works focus on simplifying the neighborhood aggregation process and model structures. However, as the depth of the models increases, the simplified models will encounter oversmoothing, resulting in a decrease in model performance. Several works leverage sophisticated learnable ...
In this paper, we introduce a self-supervised learning method to enhance the graph-level representations with the help of a set of subgraphs. For this purpose, we propose a universal framework to generate subgraphs in an auto-regressive way and then using these subgraphs to guide the learning of graph representation by Graph Neural Networks. Under this framework, we can get a comprehensive ...
And GRACE [33] introduces a framework for unsupervised graph representation learning by generating two graph views and maximizing the agreement of node representations in these two views. There are also pre-train methods [34] of using contrastive learning, which can achieve excellent results on various graph-level downstream tasks with few labels.
We study the graph representation learning problem that has emerged with the advent of numerous graph analysis tasks in the recent past. The task of representation learning from graphs of heterogeneous object attributes and complex topological structures is important yet challenging in practice. We propose an Attribute-interactive Neighborhood-aggregative Graph learning scheme (ANGraph), which ...
In recent years, graph neural networks (GNNs) have been widely used in many domains due to their powerful capability in representation learning on graph-structured data. While a majority of extant studies focus on mitigating the over-smoothing problem, recent works also reveal the limitation of GNN from a new over-correlation perspective which ...
Graph representation learning (GRL) is a powerful tool for graph analysis, which has gained massive attention from both academia and industry due to its superior performance in various real-world applications. However, the majority of existing works for GRL are dedicated to node-based tasks and thus focus on producing node representations.
While Graph Neural Network (GNN) has shown superiority in learning node representations of homogeneous graphs, leveraging GNN on heterogeneous graphs remains a challenging problem. The dominating reason is that GNN learns node representations by aggregating neighbors' information regardless of node types. Some work is proposed to alleviate such issue by exploiting relations or meta-path to ...
Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links and classifying and recommending nodes. Most embedding methods aim to preserve specific properties of the original graph in the low dimensional space. However, real-world graphs have a combination of several features that are difficult to characterize and capture by a single ...
1. Introduction. Graphs are powerful data representations that can model numerous complex systems across various areas such as social media [1], knowledge graphs [2], e-commerce transactions [3], and many other research areas [4].Because of the expressive power of graphs, a massive amount of graph-structured data has been accumulated, and graph machine learning (GML) has attracted considerable ...
Graph Convolutional Network (GCN) is increasingly becoming popular among researchers for its capability of solving the task of classification of nodes, graphs or links. Graphs being a very useful representation for several application domains are increasingly grabbing the attention of researchers. Methods are being proposed to extract meaningful information in a form which can be used by ...
such as bioinformatics, knowledge graphs, and social networks. The propagation of information in graphs is important in learning dynamic graph representations, and most of the existing methods achieve this by aggregation. However, relying only on aggregation to propagate information in dynamic graphs can result in delays in information ...
In this paper, we propose a novel end-to-end MVC method called Multi-view contrAstive clustering with Integrated Graph Aggregation and confidence enhance (MAGA).MAGA simultaneously performs multi-view information aggregation and contrast, as illustrated in Fig. 1.Here's a detailed overview of our approach: We begin by learning a latent representation for each view using view-specific ...
Bootstrapped Graph Latents (BGRL) is introduced - a graph representation learning method that learns by predicting alternative augmentations of the input and is thus scalable by design, achieving state-of-the-art performance and improving over supervised baselines where representations are shaped only through label information. Expand.
Heterogeneous graph representation learning has strong expressiveness when dealing with large-scale relational graph data, and its purpose is to effectively represent the semantic information and heterogeneous structure information of nodes in the graph. Current methods typically use shallow models to embed semantic information on low-order neighbor nodes in the graph, which prevents the ...