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Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings
2019
BMC Bioinformatics
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Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. ...
We also tested RDF2Vec on various drug knowledge graphs such as DrugBank, PharmGKB and KEGG to predict unknown drug-drug interactions. ...
Emre Guney for providing his feedback on proposed cross-validation method. ...
doi:10.1186/s12859-019-3284-5
pmid:31852427
pmcid:PMC6921491
fatcat:qd7ugkxhpzhszmk6dtv6dt63qm
Interaction Prediction Problems in Link Streams
[chapter]
2019
SpringerBriefs in Statistics
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Predicting future interactions is a crucial question in all these contexts, but the problem is traditionally addressed by merging interactions into a graph or series of graphs, called snapshots [7, 9, ...
First, one designs a model in order to make a prediction based on the fundamental assumption that future behaviors can be predicted from past observations. ...
Pairwise likeliness functions for prediction tasks From now on, we suppose that the prediction problem and its evaluation method are set, and we focus on the prediction model. ...
doi:10.1007/978-3-030-14683-2_6
fatcat:ljqkjhcwjnd6herze67kuskhu4
Learning to Detect Human-Object Interactions With Knowledge
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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In particular, we construct a knowledge graph based on the ground-truth annotations of training dataset and external source. ...
The recent advances in instance-level detection tasks lay a strong foundation for automated visual scenes understanding. However, the ability to fully comprehend a social scene still eludes us. ...
Works have been done for learning to detect HOIs with constraints from interacting object locations [13, 15] , pairwise spatial configuration [5] to scene context of instances [10, 33] . ...
doi:10.1109/cvpr.2019.00212
dblp:conf/cvpr/XuWLZK19
fatcat:5vvxz2yilrg47i4qlnqnv2xy4i
Local and Global Context-Based Pairwise Models for Sentence Ordering
[article]
2021
arXiv
pre-print
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For this task, most previous approaches have explored global context-based end-to-end methods using Sequence Generation techniques. ...
a much better understanding of the functioning of pairwise models. ...
However, the main motivation behind proposing this model for the pair order prediction task is because of a different pre-training task than BERT. ...
arXiv:2110.04291v1
fatcat:ilwqxxl4pjcutkuojvx7dcw6b4
xPACE and TASC Modeler: Tool support for data-driven context modeling
[article]
2022
arXiv
pre-print
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From a requirements engineering point of view, the elicitation of context-aware functionalities calls for context modeling, an early step that aims at understanding the application contexts and how it ...
To improve this situation, we implemented xPACE and TASC Modeler, which are tools that support the automation of context modeling based on existing contextual data. ...
The second part of the strategy takes the list of pairwise relations and builds a graph G by treating each pair as an edge of the graph. ...
arXiv:2204.06247v1
fatcat:cfdckfszjnbkxdgycsz5v5ibda
Neural Ranking Models for Document Retrieval
[article]
2021
arXiv
pre-print
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2021 accepted arXiv:2102.11903v2 fatcat:mc4gqayu5nborhfabgyxj5mvlu
These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. ...
A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. ...
Abcnn: Attention-based convolutional neural network for modeling sentence pairs. Transactions of the Association for Computational Linguistics, 4, 259-272. ...
arXiv:2102.11903v1
fatcat:zc2otf456rc2hj6b6wpcaaslsa
Constructing Narrative Event Evolutionary Graph for Script Event Prediction
[article]
2018
arXiv
pre-print
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To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. ...
Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. ...
The authors would like to thank the anonymous reviewers for the insightful comments. They also thank Haochen Chen and Yijia Liu for the helpful discussion. ...
arXiv:1805.05081v2
fatcat:ar5udtjjonahvd6c2lscnnszzi
Constructing Narrative Event Evolutionary Graph for Script Event Prediction
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. ...
Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. ...
The authors would like to thank the anonymous reviewers for the insightful comments. They also thank Haochen Chen and Yijia Liu for the helpful discussion. ...
doi:10.24963/ijcai.2018/584
dblp:conf/ijcai/LiDL18
fatcat:xbbe2mysofhflit5dqbm6gf2tm
Cartesian Kernel: An Efficient Alternative to the Pairwise Kernel
2010
IEICE transactions on information and systems
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The pairwise kernel has been proposed for those purposes by several research groups independently, and has been used successfully in several fields. ...
While the existing pairwise kernel (which we refer to as the Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can ...
Models for pairwise prediction should take a pair of in-stances as input, and output a relationship between the two instances. ...
doi:10.1587/transinf.e93.d.2672
fatcat:lsdwk4uwvnc6pge3igfnwine5m
Protein Interaction Networks: Protein Domain Interaction and Protein Function Prediction
[chapter]
2011
Handbook of Statistical Bioinformatics
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These inferences are based on the premise that the function of a protein may be discovered by studying its interaction with one or more proteins of known functions. ...
Most of a cell's functional processes involve interactions among proteins, and a key challenge in proteomics is to better understand these complex interaction graphs at a systems level. ...
(b) Global model based on pairwise kernel approach, where each edge is treated independently. (c) Local model for protein v 2 . ...
doi:10.1007/978-3-642-16345-6_21
fatcat:whl2kgd3rbfcjm3ljkd56tj7vq
Context-Aware Zero-Shot Recognition
[article]
2019
arXiv
pre-print
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The results on Visual Genome (VG) dataset show that our model significantly boosts performance with the additional visual context compared to traditional methods. ...
The proposed algorithm is evaluated on both zero-shot region classification and zero-shot detection tasks. ...
Zero-shot detection results We extend our region classification model for detection task by adding a background detector. ...
arXiv:1904.09320v3
fatcat:kpida5rvhbdsbge5lolzezszoq
Contextual Heterogeneous Graph Network for Human-Object Interaction Detection
[article]
2020
arXiv
pre-print
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Human-object interaction(HOI) detection is an important task for understanding human activity. Graph structure is appropriate to denote the HOIs in the scene. ...
In addition, a graph attention mechanism based on the intra-class context and inter-class context is exploited to improve the learning. ...
We evaluate our model on two HOI datasets: Metric. We adopt the mean average precision (mAP), which is generally used in detection tasks, for our evaluation. ...
arXiv:2010.10001v1
fatcat:myic6juxajgvdct7eh2tw5tzsa
Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions
[article]
2022
arXiv
pre-print
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To investigate the underlying mechanism, we explore the capacity of GNNs to capture pairwise interactions between nodes under contexts with different complexities, especially for their graph-level and ...
To overcome that, we propose a novel graph rewiring approach based on the pairwise interaction strengths to adjust the reception fields of each node dynamically. ...
For example, FC-graphs consist of all pairwise relations, while in KNN-graphs, some pairs of entities possess a relation and others do not. ...
arXiv:2205.07266v3
fatcat:mcplu5mksrerxaf5j76nn3i4lq
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2007.01423v1 fatcat:eanhet5qfnfpfnouda62dsxu5a
Existing algorithms for this task rely on negative sampling objectives that maximize the similarity in node embeddings at nearby nodes (referred to as "cohesion") by maintaining positive and negative corpus ...
Our approach can be used in conjunction with any GRL algorithm and we demonstrate the efficacy of our approach over baseline negative sampling methods over downstream node classification tasks on a number ...
Overall, for small networks (CiteSeer, Cora, PPI and Synthetic networks), each training epochs on average takes one minute, whereas, for medium-size networks (PubMed), each training epochs take around ...
arXiv:2007.01423v2
fatcat:yqpujnmabbeghgrsldrkqdqnba
Evaluating Modules in Graph Contrastive Learning
[article]
2022
arXiv
pre-print
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Based on this framework, we conduct controlled experiments over a wide range of architectural designs and hyperparameter settings on node and graph classification tasks. ...
performance on graph classification. ...
From this perspective, we try to investigate how the modules interact with each other and find out the bestperforming pairs. (3) Full model. ...
arXiv:2106.08171v2
fatcat:t3ruixdbazepndw2jk4cyl3yde
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