Graph embedding using multi-layer adjacent point merging model

by   Jianming Huang, et al.

For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs. These methods have achieved great success on resolving graph isomorphism problems. However, in some classification problems, the graph class depends on not only the topological similarity of the whole graph, but also constituent subgraph patterns. To this end, we propose a novel graph embedding method using a multi-layer adjacent point merging model. This embedding method allows us to extract different subgraph patterns from train-data. Then we present a flexible loss function for feature selection which enhances the robustness of our method for different classification problems. Finally, numerical evaluations demonstrate that our proposed method outperforms many state-of-the-art methods.



There are no comments yet.


page 1

page 2

page 3

page 4


LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric Space

For graph classification tasks, many methods use a common strategy to ag...

Quadratic GCN for Graph Classification

Graph Convolutional Networks (GCNs) have been extensively used to classi...

Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

Mining discriminative subgraph patterns from graph data has attracted gr...

Graph Learning for Clustering Multi-view Data

In this paper, we focus on graph learning from multi-view data of shared...

Multi-layer architecture for efficient steganalysis of Undermp3cover in multi-encoder scenario

Mp3 is a very popular audio format and hence it can be a good host for c...

Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters

Theoretical analyses for graph learning methods often assume a complete ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.