Graph embedding using multi-layer adjacent point merging model

• URL: http://arxiv.org/abs/2010.14773v2
• Date: Wed, 17 Feb 2021 06:59:48 GMT
• Title: Graph embedding using multi-layer adjacent point merging model
• Authors: Jianming Huang, Hiroyuki Kasai
• Abstract summary: We propose a novel graph embedding method using a multi-layer adjacent merging point model. This embedding method allows us to extract different subgraph patterns from train-data. numerical evaluations demonstrate that our proposed method outperforms many state-of-the-art methods.
• Score: 23.706877029336415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Learning Heuristics for the Maximum Clique Enumeration Problem Using Low Dimensional Representations [0.0]
  We use a learning framework for a pruning process of the input graph towards reducing the clique of the maximum enumeration problem. We study the role of using different vertex representations on the performance of this runtime method. We observe that using local graph features in the classification process produce more accurate results when combined with a feature elimination process.
  arXiv  Detail & Related papers  (2022-10-30T22:04:32Z)
• Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training [82.68805025636165]
  We propose to select positive graph instances directly from existing graphs in the training set. Our selection is based on certain domain-specific pair-wise similarity measurements. Besides, we develop an adaptive node-level pre-training method to dynamically mask nodes to distribute them evenly in the graph.
  arXiv  Detail & Related papers  (2022-06-23T20:12:51Z)
• Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation [27.215800308343322]
  We present the first Mixup-like graph augmentation method at the graph-level called Graph Transplant. Our method identifies the sub-structure as a mix unit that can preserve the local information. We extensively validate our method with diverse GNN architectures on multiple graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2021-11-10T11:10:13Z)
• Multi-view Contrastive Graph Clustering [12.463334005083379]
  We propose a generic framework to cluster multi-view attributed graph data. Inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method. Our simple approach outperforms existing deep learning-based methods.
  arXiv  Detail & Related papers  (2021-10-22T15:22:42Z)
• Graphon based Clustering and Testing of Networks: Algorithms and Theory [11.3700474413248]
  Network-valued data are encountered in a wide range of applications and pose challenges in learning. We present two clustering algorithms that achieve state-of-the-art results. We further study the applicability of the proposed distance for graph two-sample testing problems.
  arXiv  Detail & Related papers  (2021-10-06T13:14:44Z)
• Joint Graph Learning and Matching for Semantic Feature Correspondence [69.71998282148762]
  We propose a joint emphgraph learning and matching network, named GLAM, to explore reliable graph structures for boosting graph matching. The proposed method is evaluated on three popular visual matching benchmarks (Pascal VOC, Willow Object and SPair-71k) It outperforms previous state-of-the-art graph matching methods by significant margins on all benchmarks.
  arXiv  Detail & Related papers  (2021-09-01T08:24:02Z)
• Multilayer Graph Clustering with Optimized Node Embedding [70.1053472751897]
  multilayer graph clustering aims at dividing the graph nodes into categories or communities. We propose a clustering-friendly embedding of the layers of a given multilayer graph. Experiments show that our method leads to a significant improvement.
  arXiv  Detail & Related papers  (2021-03-30T17:36:40Z)
• Multilayer Clustered Graph Learning [66.94201299553336]
  We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. Experiments show that our method leads to a clustered clusters w.r.t. We learn a clustering algorithm for solving clustering problems.
  arXiv  Detail & Related papers  (2020-10-29T09:58:02Z)
• Line Graph Neural Networks for Link Prediction [71.00689542259052]
  We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. In this formalism, a link prediction problem is converted to a graph classification task. We propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
  arXiv  Detail & Related papers  (2020-10-20T05:54:31Z)
• Wasserstein-based Graph Alignment [56.84964475441094]
  We cast a new formulation for the one-to-many graph alignment problem, which aims at matching a node in the smaller graph with one or more nodes in the larger graph. We show that our method leads to significant improvements with respect to the state-of-the-art algorithms for each of these tasks.
  arXiv  Detail & Related papers  (2020-03-12T22:31:59Z)

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.