Work

Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods

Year: 2024

Type: conference-paper

Abstract: Graph-theoretic algorithms and graph machine learning models are essential tools for addressing many real-life problems, such as social network analysis and bioinformatics. To support large-scale graph analytics, graph-parallel systems have been actively developed for over one decade, such as Google's Pregel and Spark's GraphX, which (i) promote a think-like-a-vertex computing model and target (ii) iterative algorithms and (iii) those problems that output a value for each vertex. However, this model is too restricted for supporting the rich set of heterogeneous operations for graph analytics and machine learning that many real applications demand.

Source: ACM International Conference on Information and Knowledge Management (CIKM)

DOI: 10.1145/3627673.3679101

Authors: Da Yan, Lyuheng Yuan, Akhlaque Ahmad, Saugat Adhikari

Institution: Indiana University Bloomington

Language: English

Topic: Graph Theory and Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, U.S. Department of Energy

Awards: RII Track-4: NSF: Massively Parallel Graph Processing on Next-Generation Multi-GPU Supercomputers, Collaborative Research: OAC CORE: Federated-Learning-Driven Traffic Event Management for Intelligent Transportation Systems, Collaborative Research: OAC Core: Large-Scale Spatial Machine Learning for 3D Surface Topology in Hydrological Applications