Ra-GCN

Tian, Anqi; Zhang, Chunhong; Rang, Miao; Yang, Xueying; Zhan, Zhiqiang

doi:10.1145/3383972.3384067

Cited by 31 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Relational Graph Convolutional Networks (RGCNs) [14] extend Graph Convolutional Networks to deal with the data of heterogeneous Knowledge Graphs that contain various relation types [15].…”

Section: Graph Convolutional Networkmentioning

confidence: 99%

Comparing Methods for Drug-Gene Interaction Prediction on the Biomedical Literature Knowledge Graph: Performance vs. Explainability

Aisopos

Παλιούρας

2023

Preprint

View full text Add to dashboard Cite

This paper applies different link prediction methods on a knowledge graphgenerated from biomedical literature, with the aim to compare their ability toidentify unknown drug-gene interactions and explain their predictions. Identifyingnovel drug-target interactions is a crucial step in drug discovery and repurposing.One approach to this problem is to predict missing links between drug and genenodes, in a graph that contains relevant biomedical knowledge. Such a knowledgegraph can be extracted from biomedical literature, using text mining tools. In thiswork, we compare state-of-the-art graph embedding approaches and contextualpath analysis on the interaction prediction task. The comparison reveals atrade-off between predictive accuracy and explainability of predictions. Focusingon explainability, we train a decision tree on model predictions and show how itcan aid the understanding of the prediction process. We further test the methodson a drug repurposing task and validate the predicted interactions againstexternal databases, with very encouraging results.

show abstract

“…Relational Graph Convolutional Networks (RGCNs) [14] extend Graph Convolutional Networks to deal with the data of heterogeneous Knowledge Graphs that contain various relation types [15].…”

Section: Graph Convolutional Networkmentioning

confidence: 99%

Comparing Methods for Drug-Gene Interaction Prediction on the Biomedical Literature Knowledge Graph: Performance vs. Explainability

Aisopos

Παλιούρας

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…GCN [32], one of the most popular algorithms in this family, simplifies the GNN model using ChebNet [12]'s first-order approximation. There are numerous GCN-based variants [7,15,71]. For instance, TAGCN [15] uses multiple kernels to extract neighborhood information of different receptive fields, whereas GCN uses a single convolution kernel.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, TAGCN [15] uses multiple kernels to extract neighborhood information of different receptive fields, whereas GCN uses a single convolution kernel. RGCN [71] extended GCN to accommodate heterogeneous graphs with different types of nodes and relations. GraphSage [23] is a famous spatial approach that utilizes various aggregation functions to combine node neighborhood information.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Membership Inference Attacks Against Sequential Recommender Systems

Zhu

Fan

et al. 2023

Proceedings of the ACM Web Conference 2023

View full text Add to dashboard Cite

Recent research demonstrates that GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. However, they mainly focus on node classification tasks, neglecting the potential threats entailed within the domain of graph classification tasks. Furthermore, their practicality is questionable due to unreasonable assumptions, specifically concerning the large data requirements and extensive model knowledge. To this end, we advocate following strict settings with limited real data and hard-label awareness to generate synthetic data, thereby facilitating the stealing of the target model. Specifically, following important data generation principles, we introduce three model stealing attacks to adapt to different actual scenarios: MSA-AU is inspired by active learning and emphasizes the uncertainty to enhance query value of generated samples; MSA-AD introduces diversity based on Mixup augmentation strategy to alleviate the query inefficiency issue caused by over-similar samples generated by MSA-AU; MSA-AUD combines the above two strategies to seamlessly integrate the authenticity, uncertainty, and diversity of the generated samples. Finally, extensive experiments consistently demonstrate the superiority of the proposed methods in terms of concealment, query efficiency, and stealing performance.

show abstract

“…R-GCN [7] uses the message-passing framework of the graph convolution network to aggregate the embedding of neighboring entities and then exploit them with a decoder such as DistMult. Inheriting ideas from R-GCN, RA-GCN [8] improves the propagation formula for updating entity or node information and extracts additional entity and relationship information. ConvE [9] uses convolutional and fully connected layers to capture the interaction between entities and relations.…”

Section: Related Workmentioning

confidence: 99%

FouriER: Link Prediction by Mixing Tokens with Fourier-enhanced MetaFormer

Vu,

Ngo,

et al. 2023

ESANN 2023 Proceesdings

View full text Add to dashboard Cite

Knowledge graph link prediction has been researched for many years. With the steady development of data, the demand for missing link prediction in knowledge bases is growing. In this study, we propose FouriER, a model using Fourier transforms integrated into MetaFormer architecture to learn features from embeddings better but more computationally cost-effective than the self-attention mechanism in Transformer models. Furthermore, we transform embeddings to a 2D form and stack them that benefit the model in learning interactions between entities and relations more efficiently. As a result, we found that our model outperformed baseline models on two benchmark datasets in our experiments.

show abstract

Ra-GCN

Cited by 31 publications

References 5 publications

Comparing Methods for Drug-Gene Interaction Prediction on the Biomedical Literature Knowledge Graph: Performance vs. Explainability

Comparing Methods for Drug-Gene Interaction Prediction on the Biomedical Literature Knowledge Graph: Performance vs. Explainability

Membership Inference Attacks Against Sequential Recommender Systems

FouriER: Link Prediction by Mixing Tokens with Fourier-enhanced MetaFormer

Contact Info

Product

Resources

About