ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Integrating network embedding and community outlier detection via multiclass graph description

Bandyopadhyay, S and Vishal Vivek, S and Murty, MN (2020) Integrating network embedding and community outlier detection via multiclass graph description. In: Frontiers in Artificial Intelligence and Applications, 29 August-8 September 2020, Santiago de Compostela, Online; Spain, pp. 976-983.

fro_art_int_app_325_976-983.pdf - Published Version

Download (629kB) | Preview
Official URL: https://dx.doi.org/10.3233/FAIA200191


Network (or graph) embedding is the task to map the nodes of a graph to a lower dimensional vector space, such that it preserves the graph properties and facilitates the downstream network mining tasks. Real world networks often come with (community) outlier nodes, which behave differently from the regular nodes of the community. These outlier nodes can affect the embedding of the regular nodes, if not handled carefully. In this paper, we propose a novel unsupervised graph embedding approach (called DMGD) which integrates outlier and community detection with node embedding. We extend the idea of deep support vector data description to the framework of graph embedding when there are multiple communities present in the given network, and an outlier is characterized relative to its community. We also show the theoretical bounds on the number of outliers detected by DMGD. Our formulation boils down to an interesting minimax game between the outliers, community assignments and the node embedding function. We also propose an efficient algorithm to solve this optimization framework. Experimental results on both synthetic and real world networks show the merit of our approach compared to state-of-the-arts. © 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: Anomaly detection; Artificial intelligence; Data description; Embeddings; Statistics; Vector spaces, Community detection; Dimensional vectors; Downstream networks; Integrating networks; Optimization framework; Real-world networks; Support vector data description; Theoretical bounds, Graph theory
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 27 Nov 2020 07:55
Last Modified: 27 Nov 2020 07:55
URI: http://eprints.iisc.ac.in/id/eprint/66830

Actions (login required)

View Item View Item