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A Graph Embedding Technique for Weighted Graphs Based on LSTM Autoencoders
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논문 기본 정보

Type
Academic journal
Author
Minji Seo (숙명여자대학교) Ki Yong Lee (숙명여자대학교)
Journal
한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제16권 제6호 KCI Accredited Journals
Published
2020.1
Pages
1,407 - 1,423 (17page)

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A Graph Embedding Technique for Weighted Graphs Based on LSTM Autoencoders
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Abstract· Keywords

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A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generatea low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, therehave been studies on graph embedding, especially using deep learning techniques. However, until now, mostdeep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper,we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM)autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between thesenodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweightsequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, wecollect the encoding vectors obtained from the graph and combine them to generate the final embedding vectorfor the graph. These embedding vectors can be used to classify weighted graphs or to search for similarweighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective inmeasuring the similarity between weighted graphs.
#Graph Embedding #Graph Similarity #LSTM Autoencoder #Weighted Graph Embedding #Weighted Graph

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M. J. J. Ghrabat / 2019 / An effective image retrieval based on optimized genetic algorithm utilized a novel SVM-based convolutional neural network classifier / Human-centric Computing and Information Sciences 9:31 google schola 이대원 / 2019 / Future Trends of AI-based Smart Systems and Services: Challenges, Opportunities, and Solutions / JIPS(Journal of Information Processing Systems) 15(4):717~723 dbpia E. Gultepe / 2018 / Improving clustering performance using independent component analysis and unsupervised feature learning / Human-centric Computing and Information Sciences 8:25 google schola H. Cai / 2018 / A comprehensive survey of graph embedding : problems, techniques, and applications / IEEE Transactions on Knowledge and Data Engineering 30(9):1616~1637 google schola P. Goyal / 2018 / Graph embedding techniques, applications, and performance : a survey / Knowledge-Based Systems 151:78~94 google schola

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