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Information enhancement graph representation learning
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AI
DOI：10.1016/j.patrec.2025.04.006.png)
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Abstract
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Graph representation learning is an important and fundamental research concentration in complex networks. Graph neural networks design excellent filters and perform positively in downstream tasks. From first principles, the fundamental goal of graph representation learning is to obtain neighbor information to decrease the uncertainty of target nodes. Based on the partial information decomposition (PID), this paper finds that the existing node aggregation strategy does not obtain sufficient information gain from neighbors. Furthermore, the graph contains a huge number of nodes, making mutual information decomposition challenging. Thus, this paper defines Partial Information Decomposition on Graph (PIDG) as a coarse-grained PID, designs a gate to learn the representations for information gains from neighbor nodes, and builds Information Enhancement (IE) module, which enhances nodes' representation capabilities by combining various forms of information from neighboring nodes. This work achieves information enhancement about the nodes in a graph and is verified on authentic datasets.
Keywords:
Graph representation
PIDG
Synergy
GCN
Information on graph
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