Transformation of Node to Knowledge Graph Embeddings for Faster Link
Prediction in Social Networks
- URL: http://arxiv.org/abs/2111.09308v1
- Date: Wed, 17 Nov 2021 04:57:41 GMT
- Title: Transformation of Node to Knowledge Graph Embeddings for Faster Link
Prediction in Social Networks
- Authors: Archit Parnami, Mayuri Deshpande, Anant Kumar Mishra, Minwoo Lee
- Abstract summary: Recent advances in neural networks have solved common graph problems such as link prediction, node classification, node clustering, node recommendation.
In this work, we investigate a transformation model which converts node embeddings obtained from random walk based methods to embeddings obtained from knowledge graph methods directly without an increase in the computational cost.
- Score: 2.458658951393896
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in neural networks have solved common graph problems such as
link prediction, node classification, node clustering, node recommendation by
developing embeddings of entities and relations into vector spaces. Graph
embeddings encode the structural information present in a graph. The encoded
embeddings then can be used to predict the missing links in a graph. However,
obtaining the optimal embeddings for a graph can be a computationally
challenging task specially in an embedded system. Two techniques which we focus
on in this work are 1) node embeddings from random walk based methods and 2)
knowledge graph embeddings. Random walk based embeddings are computationally
inexpensive to obtain but are sub-optimal whereas knowledge graph embeddings
perform better but are computationally expensive. In this work, we investigate
a transformation model which converts node embeddings obtained from random walk
based methods to embeddings obtained from knowledge graph methods directly
without an increase in the computational cost. Extensive experimentation shows
that the proposed transformation model can be used for solving link prediction
in real-time.
Related papers
- Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data.
The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
arXiv Detail & Related papers (2024-01-12T17:57:07Z) - NodeFormer: A Scalable Graph Structure Learning Transformer for Node
Classification [70.51126383984555]
We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes.
The efficient computation is enabled by a kernerlized Gumbel-Softmax operator.
Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
arXiv Detail & Related papers (2023-06-14T09:21:15Z) - You Only Transfer What You Share: Intersection-Induced Graph Transfer
Learning for Link Prediction [79.15394378571132]
We investigate a previously overlooked phenomenon: in many cases, a densely connected, complementary graph can be found for the original graph.
The denser graph may share nodes with the original graph, which offers a natural bridge for transferring selective, meaningful knowledge.
We identify this setting as Graph Intersection-induced Transfer Learning (GITL), which is motivated by practical applications in e-commerce or academic co-authorship predictions.
arXiv Detail & Related papers (2023-02-27T22:56:06Z) - Towards Real-Time Temporal Graph Learning [10.647431919265346]
We propose an end-to-end graph learning pipeline that performs temporal graph construction, creates low-dimensional node embeddings, and trains neural network models in an online setting.
arXiv Detail & Related papers (2022-10-08T22:14:31Z) - Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data.
We present a novel Graph Matching based GNN Pre-Training framework, called GMPT.
The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
arXiv Detail & Related papers (2022-03-03T09:53:53Z) - COLOGNE: Coordinated Local Graph Neighborhood Sampling [1.6498361958317633]
replacing discrete unordered objects such as graph nodes by real-valued vectors is at the heart of many approaches to learning from graph data.
We address the problem of learning discrete node embeddings such that the coordinates of the node vector representations are graph nodes.
This opens the door to designing interpretable machine learning algorithms for graphs as all attributes originally present in the nodes are preserved.
arXiv Detail & Related papers (2021-02-09T11:39:06Z) - Co-embedding of Nodes and Edges with Graph Neural Networks [13.020745622327894]
Graph embedding is a way to transform and encode the data structure in high dimensional and non-Euclidean feature space.
CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space.
Our approach achieves or matches the state-of-the-art performance in four graph learning tasks.
arXiv Detail & Related papers (2020-10-25T22:39:31Z) - Line Graph Neural Networks for Link Prediction [71.00689542259052]
We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications.
In this formalism, a link prediction problem is converted to a graph classification task.
We propose to seek a radically different and novel path by making use of the line graphs in graph theory.
In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
arXiv Detail & Related papers (2020-10-20T05:54:31Z) - Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph
Link Prediction [69.1473775184952]
We introduce a realistic problem of few-shot out-of-graph link prediction.
We tackle this problem with a novel transductive meta-learning framework.
We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction.
arXiv Detail & Related papers (2020-06-11T17:42:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.