大会名称 |
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2018年 情報科学技術フォーラム(FIT) |
大会コ-ド |
F |
開催年 |
2018 |
発行日 |
2018-09-12 |
セッション番号 |
6f |
セッション名 |
機械学習(4) |
講演日 |
2018/09/21 |
講演場所(会議室等) |
D棟D23 |
講演番号 |
IF-001 |
タイトル |
Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification |
著者名 |
Chenyi Zhuang, Qiang Ma, |
キーワード |
Graph convolutional networks, Semi-supervised learning, Graph diffusion |
抄録 |
How to make computers sufficiently understand a complex graph is an important task in a range of different fields. For instances, in the fields of the Internet, social networks, biological networks, and many others, more and more structured data is becoming available. As a result, it is interesting and necessary to devise advanced methodologies to extract meaningful data from these various graphs. In this paper, we present a scalable graph convolutional networks method for graph-structured data analysis, and then apply it to solve the graph-based semi-supervised classification problem. To make computers sufficiently understand a graph, we proposed a dual graph convolutional networks method that performs graph convolution from two different views of the raw graph: (1) local-consistency-based view and (2) global-consistency-based view. |
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