research-article

Structural Graph Representations based on Multiscale Local Network Topologies

Authors:

Evgeniy Faerman,

Matthias SchubertAuthors Info & Claims

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

Pages 91 - 98

https://doi.org/10.1145/3350546.3352505

Published: 14 October 2019 Publication History

Abstract

In many applications, it is required to analyze a graph merely based on its topology. In these cases, nodes can only be distinguished based on their structural neighborhoods and it is common that nodes having the same functionality or role yield similar neighborhood structures. In this work, we investigate two problems: (1) how to create structural node embeddings which describe a node’s role and (2) how important the nodes’ roles are for characterizing entire graphs. To describe the role of a node, we explore the structure within the local neighborhood (or multiple local neighborhoods of various extents) of the node in the vertex domain, compute the visiting probability distribution of nodes in the local neighborhoods and summarize each distribution to a single number by computing its entropy. Furthermore, we argue that the roles of nodes are important to characterize the entire graph. Therefore, we propose to aggregate the role representations to describe whole graphs for graph classification tasks. Our experiments show that our new role descriptors outperform state-of-the-art structural node representations that are usually more expensive to compute. Additionally, we achieve promising results compared to advanced state-of-the-art approaches for graph classification on various benchmark datasets, often outperforming these approaches.

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Cited By

Huang JYang Y(2023)Anomaly Detection in Machining Centers Based on Graph Diffusion-Hierarchical Neighbor Aggregation NetworksApplied Sciences10.3390/app13231291413:23(12914)Online publication date: 2-Dec-2023
https://doi.org/10.3390/app132312914
Gilhuber SBusch JRotthues DFrey CSeidl T(2023)DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43412-9_5(75-91)Online publication date: 17-Sep-2023
https://doi.org/10.1007/978-3-031-43412-9_5

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Structural Graph Representations based on Multiscale Local Network Topologies

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cover image ACM Other conferences

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 2019

507 pages

ISBN:9781450369343

DOI:10.1145/3350546

Copyright © 2019 ACM.

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Published: 14 October 2019

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WI '19

WI '19: IEEE/WIC/ACM International Conference on Web Intelligence

October 14 - 17, 2019

Thessaloniki, Greece

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Cited By

Huang JYang Y(2023)Anomaly Detection in Machining Centers Based on Graph Diffusion-Hierarchical Neighbor Aggregation NetworksApplied Sciences10.3390/app13231291413:23(12914)Online publication date: 2-Dec-2023
https://doi.org/10.3390/app132312914
Gilhuber SBusch JRotthues DFrey CSeidl T(2023)DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43412-9_5(75-91)Online publication date: 17-Sep-2023
https://doi.org/10.1007/978-3-031-43412-9_5

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