Predictive Representation Learning in Motif-Based Graph Networks

Zhang, Kaiyuan; Yu, Shuo; Wan, Liangtian; Li, Jianxin; Xia, Feng

doi:10.1007/978-3-030-35288-2_15

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…An open question of motif-based graph learning is how to integrate motifs and graph learning methods in a reasonable way. Some methods optimize existing models based on graph motifs [32][33][34]. In particular, Xia et al [32] propose a motif-based high-order clustering algorithm that can effectively improve the clustering efficiency for large social networks.…”

Section: Motif-based Graph Learningmentioning

confidence: 99%

Subgraph Adaptive Structure-Aware Graph Contrastive Learning

Chen

Yin

et al. 2022

Mathematics

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Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.

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Section: Motif-based Graph Learningmentioning

confidence: 99%

Subgraph Adaptive Structure-Aware Graph Contrastive Learning

Chen

Yin

et al. 2022

Mathematics

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“…The former is a critical research issue for capturing plenteous semantic information of elements in KGs, including translational, multiplicative, graph-based, neural networks, and temporal models. The latter is the task of predicting missing facts in KGs, and the relevant deep learning methods can be divided into three types: the first is traditional path-based reasoning method, the second type is the probability graph model, and finally, the model based on representation learning [22].…”

Section: Deep Learning For Knowledge Graphsmentioning

confidence: 99%

Deep Learning Meets Knowledge Graphs: A Comprehensive Survey

Bai

et al. 2022

Preprint

Self Cite

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Knowledge Graphs (KGs) which can encode structural relations connecting two objects with one or multiple related attributes have become an increasingly popular research direction. Given the superiority of deep learning in representing complex data in continuous space, it is handy to represent KGs data, thus promoting KGs construction, representation, and application. This survey article provides a comprehensive overview of deep learning technologies and KGs by exploring research topics from diverse phases of the KGs lifecycle, such as construction, representation, and knowledge-aware application. We propose new taxonomies on these research topics for motivating cross-understanding between deep learning and KGs. Based on the above three phases, we classify the different tasks of KGs and task-related methods. Afterwards, we explain the principles of combing deep learning in various KGs steps like KGs embedding. We further discuss the contribution and advantages of deep learning applied to the different application scenarios. Finally, we summarize some critical challenges and open issues deep learning approaches face in KGs.

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MEGA: Explaining Graph Neural Networks with Network Motifs

Ding

Luo

et al. 2023

2023 International Joint Conference on Neural Networks (IJCNN)

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Predictive Representation Learning in Motif-Based Graph Networks

Cited by 3 publications

References 16 publications

Subgraph Adaptive Structure-Aware Graph Contrastive Learning

Subgraph Adaptive Structure-Aware Graph Contrastive Learning

Deep Learning Meets Knowledge Graphs: A Comprehensive Survey

MEGA: Explaining Graph Neural Networks with Network Motifs

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