Learning word vectors in Deep Walk using convolution

Chaturvedi, Iti, Cavallari, Sandro, Cambria, Erik, and Zheng, Vincent (2017) Learning word vectors in Deep Walk using convolution. In: Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. pp. 323-328. From: FLAIRS-30: 30th International Florida Artificial Intelligence Research Society Conference, 22-24 May 2017, Marco Island, FL, USA.

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Abstract

Textual queries in networks such as Twitter can have more than one label, resulting in a multi-label classification problem. To reduce computational costs, a low-dimensional representation of a large network is learned that preserves proximity among nodes in the same community. Similar to sequences of words in a sentence, DeepWalk considers sequences of nodes in a shallow graph and clustering is done using hierarchical softmax in an unsupervised manner. In this paper, we generate network abstractions at different levels using deep convolutional neural networks. Since class labels of connected nodes in a network keep changing, we consider a fuzzy recurrent feedback controller to ensure robustness to noise.

Item ID: 63350
Item Type: Conference Item (Research - E1)
ISBN: 978-1-57735-787-2
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Copyright Information: Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Date Deposited: 02 Jul 2020 00:52
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 50%
SEO Codes: 95 CULTURAL UNDERSTANDING > 9502 Communication > 950299 Communication not elsewhere classified @ 100%
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