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Algorithmic Tools for Understanding the Motif Structure of Networks Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Multi-source Inductive Knowledge Graph Transfer

Authors : Junheng Hao, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Haifeng Chen, Junghwan Rhee, Zhichuan Li, Wei Wang

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

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Abstract

Large-scale information systems, such as knowledge graphs (KGs), enterprise system networks, often exhibit dynamic and complex activities. Recent research has shown that formalizing these information systems as graphs can effectively characterize the entities (nodes) and their relationships (edges). Transferring knowledge from existing well-curated source graphs can help construct the target graph of newly-deployed systems faster and better which no doubt will benefit downstream tasks such as link prediction and anomaly detection for new systems. However, current graph transferring methods are either based on a single source, which does not sufficiently consider multiple available sources, or not selectively learns from these sources. In this paper, we propose MSGT-GNN, a graph knowledge transfer model for efficient graph link prediction from multiple source graphs. MSGT-GNN consists of two components: the Intra-Graph Encoder, which embeds latent graph features of system entities into vectors; and the graph transferor, which utilizes graph attention mechanism to learn and optimize the embeddings of corresponding entities from multiple source graphs, in both node level and graph level. Experimental results on multiple real-world datasets from various domains show that MSGT-GNN outperforms other baseline approaches in the link prediction and demonstrate the merit of attentive graph knowledge transfer and the effectiveness of MSGT-GNN.

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previous chapter Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
next chapter MULTIFORM: Few-Shot Knowledge Graph Completion via Multi-modal Contexts
Appendix
Available only for authorised users
Footnotes
1
In this paper, we use the source graph as the graph profiles for existing well-observed systems and target graph as the graph profile for new systems, which is relatively smaller than source graphs in graph size (e.g. number of nodes/edges). We assume that the number of source graphs is at least 2 and that of the target graph is 1.
 
2
In this work, the performance is relatively insensitive to L where we fix \(L=2\) for GNN modules including baselines.
 
3
Theoretically the embedding dimension of graph-level representation can be different from that of the node-level. For simplicity, we choose both dimensions are the same, that is, \(\textrm{dim}\left( \textbf{h}_G \right) = \textrm{dim}\left( \textbf{h}^{l}_{{G}_i}\right) \), where G refers to either source or target graph.
 
4
Processed DBpedia dataset are downloadable at: Link.
 
5
We use a subset of the co-author networks, which is available at https://​aminer.​org/​data#Topic-coauthor.
 
6
{en, fr, de}\(\rightarrow \)es means the source graphs are from DBpedia English, French and German KBs and the target is Spanish KB.
 
7
Default similarity between the source and target graph is based on the Jaccard index.
 
8
 
9
We point out the thread of KG embedding in Sect. 5, including TransE and recent variants [27]. The limitation of such methods is that they are transductive methods. This is generally not applicable to our inductive learning and its downstream link prediction. However, as for evaluation metrics, we follow the metrics adopted in previous work [15] for target-adapted edge prediction instead of MRR or Hit score for a different triple completion task.
 
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Metadata
Title
Multi-source Inductive Knowledge Graph Transfer
Authors
Junheng Hao
Lu-An Tang
Yizhou Sun
Zhengzhang Chen
Haifeng Chen
Junghwan Rhee
Zhichuan Li
Wei Wang
Copyright Year
2023
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-031-26389-7

Electronic ISBN: 978-3-031-26390-3

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-26390-3_10

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