Unsupervised graph representation learning(GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning methods usually adopt self-supervised learning, and embeddings are learned by solving a handcrafted auxiliary task(so-called pretext task). However, partially due to the irregular ...
Contrastive multi-view representation learning on graphs. In Proceedings of the International Conference on Machine Learning. PMLR, 4116-4126. ... Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems 33 (2020), 7793-7804. Google Scholar [50] Meiqi Zhu, Xiao ...
Motivated by this limitation, we propose a multi-view perspective and the usage of diverse pretext tasks to capture different signals in graphs into embeddings. A novel framework, denoted as Multi-view Graph Encoder (MVGE), is proposed, and a set of key designs are identified. More specifically, a set of new pretext tasks are designed to encode ...
Representation learning on graphs: methods and applications. IEEE Data Eng. Bull 40, 3(2017), 52-74. Google Scholar [10] Hassani Kaveh and Khasahmadi Amir Hosein. 2020. Contrastive multi-view representation learning on graphs. In Proceedings of the International Conference on Machine Learning. PMLR, 4116 - 4126. Google Scholar
Unsupervised graph representation learning (GRL) aims to distill diverse graph information into task-agnostic. embeddings without label supervision. Due to a lack of support from labels, recent ...
PDF Multi-View Graph Representation Learning Beyond Homophily
2.2 Learning beyond Homophily. Due to the lack of node labels, graph representation learning beyond homophily remains largely unexplored. Existing works are mainly limited to the semi-supervised GNNs for heterophily. The common philosophy behind these works is weakening the smoothing efect.
A novel framework, denoted as Multi-view Graph Encoder (MVGE), is proposed, and a set of key designs are identified that achieve significant performance improvements in three different downstream tasks, especially on graphs with heterophily. Unsupervised graph representation learning (GRL) aims at distilling diverse graph information into task-agnostic embeddings without label supervision.
Multi-View Graph Representation Learning Beyond Homophily. 04/15/2023. ∙. by Bei Lin, et al. ∙. ∙. Unsupervised graph representation learning (GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning methods usually adopt ...
Multi-View Graph Representation Learning Beyond Homophily. Bei Lin, You Li, Ning Gui, Zhuopeng Xu, Zhiwu Yu. (Submitted on 15 Apr 2023) Unsupervised graph representation learning (GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation ...
G-AILab/MVGE
Multi-View Graph Representation Learning Beyond Homophily. Paper. Multi-View Graph Representation Learning Beyond Homophily, transactions on knowledge discovery from data, 2023 Please cite this paper. Usage. Here we provide an implementation of MVGE in Python, along with a minimal execution example (on the Cora dataset). The repository is ...
A novel framework, denoted as Multi-view Graph Encoder(MVGE), is proposed, and a set of key designs are identified. More specifically, a set of new pretext tasks are designed to encode different types of signals, and a straightforward operation is propxwosed to maintain both the commodity and personalization in both the attribute and the ...
Beyond homophily
Beyond homophily: robust graph anomaly detection via neural sparsification. Information systems. Information systems applications. ... Multi-view Graph Representation Learning Beyond Homophily. Unsupervised graph representation learning (GRL) aims at distilling diverse graph information into task-agnostic embeddings without label supervision ...
1 Graph Representation Learning Beyond Node and Homophily
1 Graph Representation Learning Beyond Node and Homophily. philyYou Li, Bei Lin, Binli Luo, Ning Gui* Member, IEEE,Abstract—Unsupervised graph representation learning aims to distill various graph informati. n into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly ...
Graph Representation Learning Beyond Node and Homophily
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly under the node homophily assumption: connected nodes tend to have similar labels and optimize performance on node-centric downstream tasks. Their design is apparently against ...
Graph Representation Learning Beyond Node and Homophily
PairE, a novel unsupervised graph embedding method using two paired nodes as the basic unit of embedding to retain the high-frequency signals between nodes to support node-related and edge-related tasks is proposed. Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation ...
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs
Contrastive multi-view representation learning on graphs. In International conference on machine learning. PMLR, 4116--4126. ... Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020b. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems ...
To effectively extract and utilize the neighborhood distribution of nodes from different classes, we propose a novel Neighborhood Distribution-guided Graph Convolutional Network, namely ND-GCN, for both homophilic and heterophilic graphs based on the assumption that the neighborhood distribution of similar nodes is similar.Specifically, ND-GCN consists of three components: lower-order ...
Unsupervised graph representation learning(GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning me…
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge
Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods ...
Multi-view multi-label learning for label-specific features via GLocal
In multi-label learning (MLL), label-specific feature (LSF) learning assumes that labels are determined by their inherent characteristics. However, in multi-view multi-label learning (MVMLL), the heterogeneity problem persists within the feature space. The views with varying dimensions can result in different dimensions of extracted LSF. Existing algorithms extract the LSF for each view ...
HomoGCL: Rethinking Homophily in Graph Contrastive Learning
Contrastive Multi-View Representation Learning on Graphs. In ICML. 4116--4126. Google Scholar [11] ... Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020b. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. In NeurIPS. Google Scholar
Mammography classification with multi-view deep learning techniques
3.2.AGN4V. The second proposed architecture, named Anatomy-aware Graph Convolutional Network Four Views (AGN4V), is an extension of the AGN introduced in Liu et al. (2021b).The proposed architecture mainly consists of a backbone and two modules based on GCNs, which receive graphs obtained from the feature maps through a specific mapping function.
Graph Representation Learning Beyond Node and Homophily
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly under the node homophily assumption: connected nodes tend to have similar labels and optimize performance on node-centric downstream tasks.
Characterizing the Influence of Topology on Graph Learning Tasks
This question, which is essential for choosing a graph learning model to deploy over a graph for downstream tasks, has not yet been systematically characterized. Existing work focuses on measuring homophily [ 17 , 32 , 24 ] or edge signal-to-noise ratio [ 9 ] , based on the discrepancy of nodes and their neighbors in features or labels without ...
Triplet Contrastive Representation Learning for Unsupervised Vehicle Re
Part feature learning plays a crucial role in achieving fine-grained semantic understanding in unsupervised vehicle re-identification. However, existing approaches directly model part and global features, which can easily lead to severe gradient vanishing issues due to their unequal feature information and unreliable pseudo-labels.
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Unsupervised graph representation learning(GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning methods usually adopt self-supervised learning, and embeddings are learned by solving a handcrafted auxiliary task(so-called pretext task). However, partially due to the irregular ...
Contrastive multi-view representation learning on graphs. In Proceedings of the International Conference on Machine Learning. PMLR, 4116-4126. ... Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems 33 (2020), 7793-7804. Google Scholar [50] Meiqi Zhu, Xiao ...
Motivated by this limitation, we propose a multi-view perspective and the usage of diverse pretext tasks to capture different signals in graphs into embeddings. A novel framework, denoted as Multi-view Graph Encoder (MVGE), is proposed, and a set of key designs are identified. More specifically, a set of new pretext tasks are designed to encode ...
Representation learning on graphs: methods and applications. IEEE Data Eng. Bull 40, 3(2017), 52-74. Google Scholar [10] Hassani Kaveh and Khasahmadi Amir Hosein. 2020. Contrastive multi-view representation learning on graphs. In Proceedings of the International Conference on Machine Learning. PMLR, 4116 - 4126. Google Scholar
Unsupervised graph representation learning (GRL) aims to distill diverse graph information into task-agnostic. embeddings without label supervision. Due to a lack of support from labels, recent ...
2.2 Learning beyond Homophily. Due to the lack of node labels, graph representation learning beyond homophily remains largely unexplored. Existing works are mainly limited to the semi-supervised GNNs for heterophily. The common philosophy behind these works is weakening the smoothing efect.
A novel framework, denoted as Multi-view Graph Encoder (MVGE), is proposed, and a set of key designs are identified that achieve significant performance improvements in three different downstream tasks, especially on graphs with heterophily. Unsupervised graph representation learning (GRL) aims at distilling diverse graph information into task-agnostic embeddings without label supervision.
Download Citation | Multi-View Graph Representation Learning Beyond Homophily | Unsupervised graph representation learning(GRL) aims to distill diverse graph information into task-agnostic ...
Multi-View Graph Representation Learning Beyond Homophily. 04/15/2023. ∙. by Bei Lin, et al. ∙. ∙. Unsupervised graph representation learning (GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning methods usually adopt ...
Multi-View Graph Representation Learning Beyond Homophily. Bei Lin, You Li, Ning Gui, Zhuopeng Xu, Zhiwu Yu. (Submitted on 15 Apr 2023) Unsupervised graph representation learning (GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation ...
Multi-View Graph Representation Learning Beyond Homophily. Paper. Multi-View Graph Representation Learning Beyond Homophily, transactions on knowledge discovery from data, 2023 Please cite this paper. Usage. Here we provide an implementation of MVGE in Python, along with a minimal execution example (on the Cora dataset). The repository is ...
A novel framework, denoted as Multi-view Graph Encoder(MVGE), is proposed, and a set of key designs are identified. More specifically, a set of new pretext tasks are designed to encode different types of signals, and a straightforward operation is propxwosed to maintain both the commodity and personalization in both the attribute and the ...
Beyond homophily: robust graph anomaly detection via neural sparsification. Information systems. Information systems applications. ... Multi-view Graph Representation Learning Beyond Homophily. Unsupervised graph representation learning (GRL) aims at distilling diverse graph information into task-agnostic embeddings without label supervision ...
1 Graph Representation Learning Beyond Node and Homophily. philyYou Li, Bei Lin, Binli Luo, Ning Gui* Member, IEEE,Abstract—Unsupervised graph representation learning aims to distill various graph informati. n into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly ...
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly under the node homophily assumption: connected nodes tend to have similar labels and optimize performance on node-centric downstream tasks. Their design is apparently against ...
PairE, a novel unsupervised graph embedding method using two paired nodes as the basic unit of embedding to retain the high-frequency signals between nodes to support node-related and edge-related tasks is proposed. Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation ...
Contrastive multi-view representation learning on graphs. In International conference on machine learning. PMLR, 4116--4126. ... Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020b. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems ...
To effectively extract and utilize the neighborhood distribution of nodes from different classes, we propose a novel Neighborhood Distribution-guided Graph Convolutional Network, namely ND-GCN, for both homophilic and heterophilic graphs based on the assumption that the neighborhood distribution of similar nodes is similar.Specifically, ND-GCN consists of three components: lower-order ...
Unsupervised graph representation learning(GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning me…
Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods ...
In multi-label learning (MLL), label-specific feature (LSF) learning assumes that labels are determined by their inherent characteristics. However, in multi-view multi-label learning (MVMLL), the heterogeneity problem persists within the feature space. The views with varying dimensions can result in different dimensions of extracted LSF. Existing algorithms extract the LSF for each view ...
Contrastive Multi-View Representation Learning on Graphs. In ICML. 4116--4126. Google Scholar [11] ... Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020b. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. In NeurIPS. Google Scholar
3.2.AGN4V. The second proposed architecture, named Anatomy-aware Graph Convolutional Network Four Views (AGN4V), is an extension of the AGN introduced in Liu et al. (2021b).The proposed architecture mainly consists of a backbone and two modules based on GCNs, which receive graphs obtained from the feature maps through a specific mapping function.
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly under the node homophily assumption: connected nodes tend to have similar labels and optimize performance on node-centric downstream tasks.
This question, which is essential for choosing a graph learning model to deploy over a graph for downstream tasks, has not yet been systematically characterized. Existing work focuses on measuring homophily [ 17 , 32 , 24 ] or edge signal-to-noise ratio [ 9 ] , based on the discrepancy of nodes and their neighbors in features or labels without ...
Part feature learning plays a crucial role in achieving fine-grained semantic understanding in unsupervised vehicle re-identification. However, existing approaches directly model part and global features, which can easily lead to severe gradient vanishing issues due to their unequal feature information and unreliable pseudo-labels.