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CONNEXIONs H2020 Project

Part of EU Open Research Repository
Published August 30, 2021 | Version v1
Conference paper Open

Random-Walk Graph Embeddings and the Influence of Edge Weighting Strategies in Community Detection Tasks

Creators

  • 1. CERTH

Description

Graph embedding methods have been developed over recent years with the goal of mapping graph data structures into low dimensional vector spaces so that conventional machine learning tasks can be efficiently evaluated. In particular, random walk based methods sample the graph using random walk sequences that capture a graph's structural properties. In this work, we study the influence of edge weighting strategies that bias the random walk process and we are able to demonstrate that under several settings the biased random walks enhance downstream community detection tasks.

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2021_OASIS_Certh.pdf

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Additional details

Funding

PREVISION – Prediction and Visual Intelligence for Security Information 833115
European Commission
CONNEXIONs – InterCONnected NEXt-Generation Immersive IoT Platform of Crime and Terrorism DetectiON, PredictiON, InvestigatiON, and PreventiON Services 786731
European Commission