Multiple Graph Convolutional Networks for Co-Saliency Detection

Jiang, Bo; Jiang, Xingyue; Tang, Jin; Luo, Bin; Huang, Shilei

doi:10.1109/icme.2019.00065

Cited by 29 publications

(19 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we study the relationship between different networks. Multi-graph relationships have been studied in many fields, such as bio-informatics [18], Predicting Disease Outcomes [13], co-saliency detection [19], recommendation [11], [12], etc. Based on the maximum retention of the network structure, [11] uses tag and contact network by selecting important features from each network to align the related networks.…”

Section: B Multi-relational Graphmentioning

confidence: 99%

“…E.g, ifÃ f (i, j) = 1.0,Ã c (i, j) = 0.5,Ã t (i, j) = 0.8, then L min = 0.5, L max = 1.0. Compared with [13] and [19], we fuse the Laplace matrix with the structure information of the graph directly instead of performing the convolution operation directly on a single graph to learn the features and then concat them. Our method may learn the structure information of different graphs better by considering the fusion of Laplace matrix.…”

Section: ) Fusion Modementioning

confidence: 99%

“…In the selection of comparison methods, the time sequence of the relevant methods and the applicability of the method to the problems proposed in this paper are considered. The baselines are MF [40], BPRMF [2], NCF [3], a friend recommendation method based on CNN, named BayDNN [6], GCN-cat [19], and the comparison of several single relationship graph of GCN. In summary, there are three methods based on matrix factorization and five methods based on neural networks.…”

Section: ) Baselinesmentioning

confidence: 99%

“…• GCN-cat: GCN-cat [19] work well in image filed for Co-Saliency Detection. In dealing with multiple graphs, it uses GCN operations on single graph, and then concatenates the results of those graphs.…”

Section: ) Baselinesmentioning

confidence: 99%

See 3 more Smart Citations

Friend Recommendation Based on Multi-Social Graph Convolutional Network

et al. 2020

View full text Add to dashboard Cite

Friend recommendations based on social relationships have attracted thousands of research under the rapid development of social networks. However, most of the existing friend recommendation methods use user attributes or a single social network, while rarely integrating multiple social relationships to enhance the representation. This paper focuses on integrating various social relationships to guide the representation learning, and further generating personalized friend recommendations. We design an end-toend framework based on multiple social networks to learn the potential features of users and construct a friend recommendation model named Multi-Social Graph Convolutional Network (MSGCN). It learns the features of higher-order neighbors from multiple social networks to enrich the representation of the target user based on the improved graph convolution neural network. In particular, some graph fusion strategies by adjusting and fusing the Laplace matrix of the graph are designed to integrate social relationships. Finally, we use Bayesian theory to transform friend recommendation into a sorting problem for personalized recommendation. The experimental results show that the proposed model outperforms the state-of-the-art methods.

show abstract

Section: B Multi-relational Graphmentioning

confidence: 99%

Section: ) Fusion Modementioning

confidence: 99%

Section: ) Baselinesmentioning

confidence: 99%

Section: ) Baselinesmentioning

confidence: 99%

See 2 more Smart Citations

Friend Recommendation Based on Multi-Social Graph Convolutional Network

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In addition, some recent works also pay attention on feature-level fusion for multi-graph data learning. For example, Jiang et al [14] present Multiple Graph Convolutional Network (MGCN) for image co-saliency estimation. Schlichtkrull et al [12] develop R-GCNs which aggregates node features from each graph via different weights and then merges them together via a simple addition operation.…”

Section: Introductionmentioning

confidence: 99%

MGARL: Multiple Graph Adversarial Regularized Learning

Zhang

Jiang

Luo

2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

Self Cite

View full text Add to dashboard Cite

Graph Convolutional Networks (GCNs) have been commonly studied for graph learning tasks, such as semi-supervised learning, clustering etc. However, many existing GCNs are generally conducted on single graph data and thus can not be applied directly to multi-graph data that consists of various types of edges between nodes. To address this issue, in this paper, we propose a novel multiple Graph Adversarial Regularized Learning (mGARL) framework for multi-graph data representation and learning. mGARL aims to learn an optimal structure invariant/consistent representation for multiple graphs by employing a novel Encoder-Decoder architecture with adversarial learning regularization. It can incorporate the structural information of multiple graphs simultaneously for the node's representation. We apply the proposed mGARL on the multi-view semi-supervised learning tasks. Experimental results on several datasets demonstrate the effectiveness and benefits of the proposed mGARL model.

show abstract

Self-supervised Co-salient Object Detection via Feature Correspondences at Multiple Scales

Chakraborty,

Samaras

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Multiple Graph Convolutional Networks for Co-Saliency Detection

Cited by 29 publications

References 23 publications

Friend Recommendation Based on Multi-Social Graph Convolutional Network

Friend Recommendation Based on Multi-Social Graph Convolutional Network

MGARL: Multiple Graph Adversarial Regularized Learning

Self-supervised Co-salient Object Detection via Feature Correspondences at Multiple Scales

Contact Info

Product

Resources

About