[1]XIAO Tianlong,XU Ji,WANG Guoyin.A multi-view and multi-granularity graph representation learning framework based on partial order relations[J].CAAI Transactions on Intelligent Systems,2025,20(1):243-254.[doi:10.11992/tis.202406010]
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A multi-view and multi-granularity graph representation learning framework based on partial order relations

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 1 Page number: 243-254 Column: 人工智能院长论坛 Public date: 2025-01-05
Title:
A multi-view and multi-granularity graph representation learning framework based on partial order relations
Author(s):
XIAO Tianlong1; 2;  XU Ji1;  WANG Guoyin3
1. State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China;
2. College of Computer Science and Technology, Guizhou University, Guiyang 550025, China;
3. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Keywords:
graph neural networks; graph pooling; multi-granularity; partial order relationships; node classification task; graph representation learning; semi-supervised learning; graph embedding
CLC:
TP391
DOI:
10.11992/tis.202406010
Abstract:
Graph pooling, as a crucial component of graph neural networks (GNNs), plays a vital role in capturing multi-granularity information of graphs. However, current graph pooling operations typically treat data points equally, often neglecting the partial order relationships among data within neighborhoods, which leads to the disruption of graph structural information. To address this issue, we propose a novel framework for multi-view and multi-granularity graph representation learning based on partial order relationships, named MVMGr-PO. This framework comprehensively scores nodes from the perspectives of node feature view, graph structure view, and global view, and then performs down-sampling operations based on the partial order relationships among nodes. Compared with other graph representation learning methods, MVMGr-PO effectively extracts multi-granularity graph structural information, thus providing a more comprehensive representation of the intrinsic structure and attributes of the graph. Additionally, MVMGr-PO can integrate various graph neural network (GNN) architectures, including graph convolutional network (GCN), graph attention network (GAT), and graph sample and aggregate (GraphSAGE). Experimental evaluations on six datasets demonstrate that compared with existing baseline models, MVMGr-PO significantly improves classification accuracy.
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