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This work proposes HSO-GAT, a topology-adaptive hyperbolic graph attention network that unifies geometric adaptation and message propagation and establishes the monotonic sensitivity of edge-level HSO to degree imbalance and analyzes the validity and radial scaling properties of node-adaptive hyperbolic mappings.
This work proposes COllabOrative Knowledge Extraction and integRation (COOKER) method for GDIL to mine inter-domain relationships and uncover the potential of domain-agnostic representations, which significantly outperforms existing baselines.
Graph Domain Incremental Learning (GDIL) aims to acquire knowledge from a continuous stream of graph domains while mitigating catastrophic forgetting. While parameter-isolation methods leveraging graph parameter-efficient adaptation show promise, prompt-based techniques struggle to adapt to GDIL, and low-rank adaptatio...
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