Influence of Random Walk Parametrization on Graph Embeddings

• Published in: Advances in Information Retrieval Vol. 12036; pp. 58 - 65
• Main Authors: Schliski, Fabian, Schlötterer, Jörg, Granitzer, Michael
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Feature learning; Graph embedding; Random walk
• DOI: 10.1007/978-3-030-45442-5_8
• ISBN: 9783030454418; 303045441X
• ISSN: 0302-9743

Network or graph embedding has gained increasing attention in the research community during the last years. In particular, many methods to create graph embeddings using random walk based approaches have been developed. node2vec [10] introduced means to control the random walk behavior, guiding the walks. We aim to reproduce parts of their work and introduce two additional modifications (jump probabilities and attention to hubs), in order to investigate how guiding and modifying the walks influences the learned embeddings. The reproduction includes the case study illustrating homophily and structural equivalence subject to the chosen strategy and a node classification task. We were not able to illustrate structural equivalence and further results show that modifications of the walks only slightly improve node classification, if at all.