Median activation functions for graph neural networks
Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs. By replacing regular convolutions with linear shift-invariant graph filters (LSI-GFs), GNNs take into account the (irregular) structure of the graph and provide meaningful representations of network data. However, LSI-GFs fail to encode local nonlinear graph signal behavior, and so do regular activation functions, which are nonlinear but pointwise. To address this issue, we propose median activation functions with support on graph neighborhoods instead of individual nodes. A GNN architecture with a trainable multirresolution version of this activation function is then tested on synthetic and real-word datasets, where we show that median activation functions can improve GNN capacity with marginal increase in complexity.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Invariance-Preserving Localized Activation Functions for Graph Neural Networks
Graph signals are signals with an irregular structure that can be described by a graph. Graph neural networks (GNNs) are information processing architectures tailored to these graph signals and made of stacked layers tha…
Authorship AttributionRecommendation SystemsKolmogorov-Arnold Networks in Low-Data Regimes: A Comparative Study with Multilayer Perceptrons
Multilayer Perceptrons (MLPs) have long been a cornerstone in deep learning, known for their capacity to model complex relationships. Recently, Kolmogorov-Arnold Networks (KANs) have emerged as a compelling alternative, …
Kolmogorov-Arnold NetworksMedian evidential c-means algorithm and its application to community detection
Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fu…
ClusteringCommunity DetectionGraph ClusteringPrototype SelectionDiGRAF: Diffeomorphic Graph-Adaptive Activation Function
In this paper, we propose a novel activation function tailored specifically for graph data in Graph Neural Networks (GNNs). Motivated by the need for graph-adaptive and flexible activation functions, we introduce DiGRAF,…
Computational EfficiencyGraph-Adaptive Activation Functions for Graph Neural Networks
Activation functions are crucial in graph neural networks (GNNs) as they allow defining a nonlinear family of functions to capture the relationship between the input graph data and their representations. This paper propo…
Recommendation Systems