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A multilayered informative randomwalk for attributed social network embedding

Bandyopadhyay, S and Biswas, A and Kara, H and Murty, MN (2020) A multilayered informative randomwalk for attributed social network embedding. In: Frontiers in Artificial Intelligence and Applications, 29 August-8 September 2020, Santiago de Compostela, Online; Spain, pp. 1738-1745.

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Official URL: https://dx.doi.org/10.3233/FAIA200287


Network representation learning (also known as Graph embedding) is a technique to map the nodes of a network to a lower dimensional vector space. Random walk based representation techniques are found to be efficient as they can easily preserve different orders of proximities between the nodes in the embedding space. Most of the social networks now-a-days have some content (or attributes) associated with each node. These attributes can provide complementary information along with the link structure of the network. But in a real life network, the information carried by the link structure and that by the attributes vary significantly over the nodes. Most of the existing unsupervised attributed network embedding algorithms do not distinguish between the link structure and the attributes of a node depending on their informativeness. In this work, we propose an unsupervised node embedding technique that exploits both the structure and attributes by intelligently prioritizing one of them, in the random walk, for each node separately. We convert the network into a multi-layered graph and propose a novel random walk based on the informativeness of a node in different layers. This unified approach is simple and computationally fast, yet able to use the content as a complement to structure and viceversa. Experimental evaluations on four real world publicly available datasets show the merit of our approach (up to 168.75 improvement) compared to the state-of-the-art algorithms in the domain. We make the source code available to download. © 2020 The authors and IOS Press.

Item Type: Conference Paper
Publication: Frontiers in Artificial Intelligence and Applications
Publisher: IOS Press BV
Additional Information: cited By 0; Conference of 24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 ; Conference Date: 29 August 2020 Through 8 September 2020; Conference Code:162625
Keywords: Embeddings; Graph theory; Random processes; Vector spaces, Dimensional vectors; Embedding technique; Experimental evaluation; Network embedding; Network representation; Real-life networks; Representation techniques; State-of-the-art algorithms, Artificial intelligence
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 27 Nov 2020 11:51
Last Modified: 27 Nov 2020 11:51
URI: http://eprints.iisc.ac.in/id/eprint/66831

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