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DSSLP: A Distributed Framework for Semi-supervised Link Prediction
Link prediction is widely used in a variety of industrial applications, ...
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Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Many practical graph problems, such as knowledge graph construction and ...
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings
This paper examines the challenging problem of learning representations ...
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Deep Generative Models for Relational Data with Side Information
We present a probabilistic framework for overlapping community discovery...
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Predicting Semantic Relations using Global Graph Properties
Semantic graphs, such as WordNet, are resources which curate natural lan...
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Graph Neighborhood Attentive Pooling
Network representation learning (NRL) is a powerful technique for learni...
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A Recurrent Graph Neural Network for Multi-Relational Data
The era of data deluge has sparked the interest in graph-based learning ...
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Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks
A neighborhood graph, which represents the instances as vertices and their relations as weighted edges, is the basis of many semi-supervised and relational models for node labeling and link prediction. Most methods employ a sequential process to construct the neighborhood graph. This process often consists of generating a candidate graph, pruning the candidate graph to make a neighborhood graph, and then performing inference on the variables (i.e., nodes) in the neighborhood graph. In this paper, we propose a framework that can dynamically adapt the neighborhood graph based on the states of variables from intermediate inference results, as well as structural properties of the relations connecting them. A key strength of our framework is its ability to handle multi-relational data and employ varying amounts of relations for each instance based on the intermediate inference results. We formulate the link prediction task as inference on neighborhood graphs, and include preliminary results illustrating the effects of different strategies in our proposed framework.
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