Batch normalization layers are introduced for neural networks on complex domains with practical implementations for the Siegel disk and tested on radar, graph, and action recognition tasks.
10 Batch Normalization for Neural Networks on Complex Domains Table 5.The main notations used in our paper
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Link prediction is a key problem for network-structured data. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihood of links. They have obtained wide practical uses due to their simplicity, interpretability, and for some of them, scalability. However, every heuristic has a strong assumption on when two nodes are likely to link, which limits their effectiveness on networks where these assumptions fail. In this regard, a more reasonable way should be learning a suitable heuristic from a given network instead of using predefined ones. By extracting a local subgraph around each target link, we aim to learn a function mapping the subgraph patterns to link existence, thus automatically learning a `heuristic' that suits the current network. In this paper, we study this heuristic learning paradigm for link prediction. First, we develop a novel $\gamma$-decaying heuristic theory. The theory unifies a wide range of heuristics in a single framework, and proves that all these heuristics can be well approximated from local subgraphs. Our results show that local subgraphs reserve rich information related to link existence. Second, based on the $\gamma$-decaying theory, we propose a new algorithm to learn heuristics from local subgraphs using a graph neural network (GNN). Its experimental results show unprecedented performance, working consistently well on a wide range of problems.
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Exploratory application of GCN, GAT and GraphSAGE to building footprint simplification (node movement prediction) and aggregation (link prediction) on multi-scale datasets, with GraphSAGE stronger on links but persistent challenges in node accuracy and greater difficulty for aggregation.
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.
citing papers explorer
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Batch Normalization for Neural Networks on Complex Domains
Batch normalization layers are introduced for neural networks on complex domains with practical implementations for the Siegel disk and tested on radar, graph, and action recognition tasks.
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Towards Graph-Based Deep Learning for Map Generalization: Insights from Building Footprints Simplification and Aggregation
Exploratory application of GCN, GAT and GraphSAGE to building footprint simplification (node movement prediction) and aggregation (link prediction) on multi-scale datasets, with GraphSAGE stronger on links but persistent challenges in node accuracy and greater difficulty for aggregation.
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TabEmb: Joint Semantic-Structure Embedding for Table Annotation
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.