TY - GEN
T1 - An Advanced Graph Embedding Framework with Node Embedding to Design Water Pipeline Networks
AU - Rukmi, Alvida Mustika
AU - Shiddiqi, Ary Mazharuddin
AU - Saikhu, Ahmad
AU - Fransiska, Muhammad Jefri
AU - Mazeau, Leo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper introduces a novel approach to developing pipeline networks using graph embedding techniques. The proposed framework involves creating a graph representation of the pipeline network and embedding it into a lower-dimensional space. By node embedding, as a part of graph embedding, our approach applies the relation and closeness between nodes to obtain network optimization by minimum costs while satisfying the demand points represented by nodes on the graph. While node embedding generation, the approach uses a random walk process to generate sequences of nodes and a skipgram process to train neighbors of a node. Spectral and Kruskal's algorithm supports the optimization of network layout. This contribution adds to the existing body of research on graph embedding and its application in network optimization, offering a new perspective on the development of pipeline networks. Our results highlight the potential of graph embedding as an effective tool for engineers and planners in designing pipeline networks that meet community needs, minimize environmental impact, and maximize sustainability.
AB - This paper introduces a novel approach to developing pipeline networks using graph embedding techniques. The proposed framework involves creating a graph representation of the pipeline network and embedding it into a lower-dimensional space. By node embedding, as a part of graph embedding, our approach applies the relation and closeness between nodes to obtain network optimization by minimum costs while satisfying the demand points represented by nodes on the graph. While node embedding generation, the approach uses a random walk process to generate sequences of nodes and a skipgram process to train neighbors of a node. Spectral and Kruskal's algorithm supports the optimization of network layout. This contribution adds to the existing body of research on graph embedding and its application in network optimization, offering a new perspective on the development of pipeline networks. Our results highlight the potential of graph embedding as an effective tool for engineers and planners in designing pipeline networks that meet community needs, minimize environmental impact, and maximize sustainability.
KW - graph embedding
KW - node embedding
KW - optimization
KW - spectral clustering
KW - water pipeline networks
KW - word2vec
UR - http://www.scopus.com/inward/record.url?scp=85180373563&partnerID=8YFLogxK
U2 - 10.1109/ICTS58770.2023.10330890
DO - 10.1109/ICTS58770.2023.10330890
M3 - Conference contribution
AN - SCOPUS:85180373563
T3 - 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
SP - 243
EP - 248
BT - 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Information and Communication Technology and System, ICTS 2023
Y2 - 4 October 2023 through 5 October 2023
ER -