4.1 Graph convolutional network Graph Convolution Networks (GCN) [5] is a semi-supervised learning approach for Graph-based data, e.g., document type in a citation network. The approach is based on the efficient usage of CNNs, which work directly on graphs. The Convolutional architecture is ...
The guessing number of a directed graph (digraph), equivalent to the entropy of that digraph, was introduced as a direct criterion on the solvability of a network coding instance. This paper makes two contributions on the guessing number. First, we introduce an undirected graph on all possible...
标签:Inductive Graph Embedding 概述:针对以往transductive的方式(不能表示unseen nodes)的方法作了改进,提出了一种inductive的方式改进这个问题,该方法学习聚合函数,而不是某个节点的向量表示。 链接:https://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf 相关数据集: Citation Re...
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Graph Embedding. WSDM 2021 Nearly Linear Time Algorithm for Mean Hitting Times of Random Walks on a Graph. WSDM 2021 Why Do Attributes Propagate in Graph Convolutional Neural Networks? AAAI 2021 ...
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network Lamb, "Multitask learning on graph neural networks-learning multiple graph centrality measures with a unified network," arXiv preprint arXiv:1809.07695, ... P Avelar,H Lemos,M Prates,.....
Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) a
F Parise,A Ozdaglar 摘要: In this paper, we present a unifying framework for analyzing equilibria and designing interventions for large network games sampled from a stochastic network formation process represented by a graphon. We first introduce a new class of infinite population games, termed ...
We introduced a multi-modal framework for inferring causal relations in brain networks, based on a graph neural network architecture, uniting structural and functional information observed with DTI and fMRI. First this model provides a data-driven perspective on a fundamental question in neuroscience, ...
We propose a novel graph cross network (GXN) to achieve comprehensive feature learning from multiple scales of a graph. Based on trainable hierarchical representations of a graph, GXN enables the interchange of intermediate features across scales to promote information flow. Two key ingredients of GXN...
Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graph