One Node at a Time: Node-Level Network Classification

• URL: http://arxiv.org/abs/2208.02162v1
• Date: Wed, 3 Aug 2022 15:48:39 GMT
• Title: One Node at a Time: Node-Level Network Classification
• Authors: Saray Shai, Isaac Jacobs, Peter J. Mucha
• Abstract summary: We study the connection between classification of a network and of its constituent nodes. We show that a classifier can be trained to accurately predict the network category of a given node. We discuss two applications of node-level network classification: (i) whole-network classification from small samples of nodes, and (ii) network bootstrapping.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network classification aims to group networks (or graphs) into distinct categories based on their structure. We study the connection between classification of a network and of its constituent nodes, and whether nodes from networks in different groups are distinguishable based on structural node characteristics such as centrality and clustering coefficient. We demonstrate, using various network datasets and random network models, that a classifier can be trained to accurately predict the network category of a given node (without seeing the whole network), implying that complex networks display distinct structural patterns even at the node level. Finally, we discuss two applications of node-level network classification: (i) whole-network classification from small samples of nodes, and (ii) network bootstrapping.

Related papers

• GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection [51.608147732998994]
  Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. We propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features.
  arXiv  Detail & Related papers  (2024-01-17T13:04:23Z)
• Collaborative Graph Neural Networks for Attributed Network Embedding [63.39495932900291]
  Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. We propose COllaborative graph Neural Networks--CONN, a tailored GNN architecture for network embedding.
  arXiv  Detail & Related papers  (2023-07-22T04:52:27Z)
• Nested stochastic block model for simultaneously clustering networks and nodes [9.860884833526407]
  We introduce the nested block model (NSBM) to cluster a collection of networks while simultaneously detecting communities within each network. NSBM has several appealing features including the ability to work on unlabeled networks with potentially different node sets.
  arXiv  Detail & Related papers  (2023-07-18T12:46:34Z)
• Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks [15.895606627146291]
  In this study, we propose an end-to-end deep embedded clustering model for attributed networks. It utilizes graph autoencoder and node attribute autoencoder to respectively learn node representations and cluster assignments. The proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-28T02:35:34Z)
• A Variational Edge Partition Model for Supervised Graph Representation Learning [51.30365677476971]
  This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities. We partition each edge into the summation of multiple community-specific weighted edges and use them to define community-specific GNNs. A variational inference framework is proposed to jointly learn a GNN based inference network that partitions the edges into different communities, these community-specific GNNs, and a GNN based predictor that combines community-specific GNNs for the end classification task.
  arXiv  Detail & Related papers  (2022-02-07T14:37:50Z)
• DINE: A Framework for Deep Incomplete Network Embedding [33.97952453310253]
  We propose a Deep Incomplete Network Embedding method, namely DINE. We first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. We evaluate DINE over three networks on multi-label classification and link prediction tasks.
  arXiv  Detail & Related papers  (2020-08-09T04:59:35Z)
• Graph Prototypical Networks for Few-shot Learning on Attributed Networks [72.31180045017835]
  We propose a graph meta-learning framework -- Graph Prototypical Networks (GPN) GPN is able to perform textitmeta-learning on an attributed network and derive a highly generalizable model for handling the target classification task.
  arXiv  Detail & Related papers  (2020-06-23T04:13:23Z)
• Unsupervised Differentiable Multi-aspect Network Embedding [52.981277420394846]
  We propose a novel end-to-end framework for multi-aspect network embedding, called asp2vec. Our proposed framework can be readily extended to heterogeneous networks.
  arXiv  Detail & Related papers  (2020-06-07T19:26:20Z)
• Progressive Graph Convolutional Networks for Semi-Supervised Node Classification [97.14064057840089]
  Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. We propose a method to automatically build compact and task-specific graph convolutional networks.
  arXiv  Detail & Related papers  (2020-03-27T08:32:16Z)
• Adversarial Deep Network Embedding for Cross-network Node Classification [27.777464531860325]
  Cross-network node classification leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network. In this paper, we propose an adversarial cross-network deep network embedding model to integrate adversarial domain adaptation with deep network embedding. The proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.
  arXiv  Detail & Related papers  (2020-02-18T04:30:43Z)
• A Block-based Generative Model for Attributed Networks Embedding [42.00826538556588]
  We propose a block-based generative model for attributed network embedding from a probability perspective. We use a neural network to characterize the nonlinearity between node embeddings and node attributes. The results show that our proposed method consistently outperforms state-of-the-art embedding methods for both clustering and classification tasks.
  arXiv  Detail & Related papers  (2020-01-06T03:44:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.