Learning Representations using Spectral-Biased Random Walks on Graphs

Sharma, Sharma and Chauhan, Jatin and Kaul, Manohar (2020) Learning Representations using Spectral-Biased Random Walks on Graphs. In: Proceedings of the International Joint Conference on Neural Networks, 19 July 2020 - 24 July 2020.

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Abstract

Several state-of-the-art neural graph embedding methods are based on short random walks (stochastic processes) because of their ease of computation, simplicity in capturing complex local graph properties, scalability, and interpretibility. In this work, we are interested in studying how much a probabilistic bias in this stochastic process affects the quality of the nodes picked by the process. In particular, our biased walk, with a certain probability, favors movement towards nodes whose neighborhoods bear a structural resemblance to the current node's neighborhood. We succinctly capture this neighborhood as a probability measure based on the spectrum of the node's neighborhood subgraph represented as a normalized Laplacian matrix. We propose the use of a paragraph vector model with a novel Wasserstein regularization term. We empirically evaluate our approach against several state-of-the-art node embedding techniques on a wide variety of real-world datasets and demonstrate that our proposed method significantly improves upon existing methods on both link prediction and node classification tasks.

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IITH Creators:

IITH Creators	ORCiD
Sharma, Sharma	UNSPECIFIED
Chauhan, Jatin	UNSPECIFIED
Kaul, Manohar	UNSPECIFIED

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

Biased random walk; Classification tasks; Embedding technique; Graph properties; Normalized Laplacian; Probability measures; Real-world datasets; Regularization terms;Classification (of information); Embeddings; Matrix algebra; Random processes; Stochastic systems

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

11 Aug 2021 05:09

Last Modified:

11 Aug 2021 05:09

URI:

http://raiithold.iith.ac.in/id/eprint/8791