Interpreting Node Embedding with Text-labeled Graphs

Serra，Giuseppe1,2; Xu，Zhao1; Niepert，Mathias1; Lawrence，Carolin1; Tino，Peter2; Yao，Xin2,3

10.1109/IJCNN52387.2021.9533692

2021-07-18

International Joint Conference on Neural Networks (IJCNN)

2161-4393

978-1-6654-4597-9

Proceedings of the International Joint Conference on Neural Networks

2021-July

1-8

JUL 18-22, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Graph neural networks have recently received increasing attention. These methods often map nodes into latent spaces and learn vector representations of the nodes for a variety of downstream tasks. To gain trust and to promote collaboration between AIs and humans, it would be better if those representations were interpretable for humans. However, most explainable AIs focus on a supervised learning setting and aim to answer the following question: 'Why does the model predict y for an input x?'. For an unsupervised learning setting as node embedding, interpretation can be more complicated since the embedding vectors are usually not understandable for humans. On the other hand, nodes and edges in a graph are often associated with texts in many real-world applications. A question naturally arises: could we integrate the human-understandable textural data into graph learning to facilitate interpretable node embedding? In this paper we present interpretable graph neural networks (iGNN), a model to learn textual explanations for node representations modeling the extra information contained in the associated textual data. To validate the performance of the proposed method, we investigate the learned interpretability of the embedding vectors and use functional interpretability to measure it. Experimental results on multiple text-labeled graphs show the effectiveness of the iGNN model on learning textual explanations of node embedding while performing well in downstream tasks.

interpretability Node embedding text mining

其他

英语

CPCI-S ; EI

European Union[766186]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic

WOS:000722581703025

20214110995552

Data mining ; Graph neural networks ; Graph theory ; Vector spaces

Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Mathematics:921 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

2-s2.0-85116481492

Scopus

1.Nec Laboratories Europe,Heidelberg,69115,Germany
2.Cercia,School of Computer Science,University of Birmingham,United Kingdom
3.Southern University of Science and Technology,Shenzhen,China

Serra，Giuseppe,Xu，Zhao,Niepert，Mathias,et al. Interpreting Node Embedding with Text-labeled Graphs[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:1-8.