2026-09-18T23:15:16Zhttps://api.mountainscholar.org/server/oai/request

oai:mountainscholar.org:10217/2448172026-06-11T18:13:02Zcom_10217_100532com_10217_100000com_10217_100411com_10217_100303col_10217_182111col_10217_100415

Yasodara, Hansi, author Jayasumana, Anura, advisor Pasricha, Sudeep, committee member Ray, Indrakshi, committee member 2026-06-08T10:31:46Z 2026 PaththiniHettiArachchige_colostate_0053N_19566.pdf https://hdl.handle.net/10217/244817 https://doi.org/10.25675/3.027177 A comprehensive framework for efficient and scalable graph representation learning is presented, emphasizing coordinate-based and explicit structural methods. The research addresses the limitations of Graph Neural Networks (GNNs) in resource-constrained environments, including edge devices and large-scale deployments, by developing lightweight, non-neural alternatives. The first contribution is the Network Feature Embedding (NFE) pipeline, which integrates diffusion-based, positional, and structural descriptors into a unified representation for node classification. The second contribution is the Topology Coordinate-Driven Random Forests (TC-DRF) framework, which combines anchor-based topology coordinates with Random Forest classifiers for graph-level learning and cross-dataset transfer. Extensive evaluations of NFE and TC-DRF on vision, molecular, and social graph benchmarks demonstrate competitive predictive performance while substantially reducing computational overhead, memory footprint, and energy consumption. The proposed frameworks enable zero-shot cross-dataset transfer, maintain robustness under class imbalance, and support practical deployment in Green AI settings. Edge-device experiments, including deployment on Raspberry Pi hardware, confirm sub-millisecond inference latency and ultra-low energy usage. This research challenges the prevailing reliance on deep message-passing architectures for graph learning, demonstrating that explicit structural representations coupled with lightweight models provide viable, interpretable, and resource-efficient alternatives. The findings contribute to the advancement of scalable and sustainable graph learning methodologies and establish a foundation for future work in structural embeddings, dynamic graph analysis, and hybrid structural-attribute learning models. born digital masters theses English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. graph learning green AI transfer learning graph representation learning graph embedding neural networks (GENNs) topology-aware learning Graph feature engineering and coordinate-based learning for transferable and energy-efficient artificial intelligence Text Master of Science (M.S.) Masters Electrical and Computer Engineering Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access