Molecular Classification Using Hyperdimensional Graph Classification

• URL: http://arxiv.org/abs/2403.12307v1
• Date: Mon, 18 Mar 2024 23:16:17 GMT
• Title: Molecular Classification Using Hyperdimensional Graph Classification
• Authors: Pere Verges, Igor Nunes, Mike Heddes, Tony Givargis, Alexandru Nicolau,
• Abstract summary: This work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. An important application within this domain involves the identification of cancerous cells across diverse molecular structures. We propose an HDC-based model that demonstrates comparable Area Under the Curve results when compared to state-of-the-art models like Graph Neural Networks (GNNs) or the Weisfieler-Lehman graph kernel (WL)
• Score: 41.38562343472387
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained significant attention. This is notable in the field of chemoinformatics, where learning from graph representations plays a pivotal role. An important application within this domain involves the identification of cancerous cells across diverse molecular structures. We propose an HDC-based model that demonstrates comparable Area Under the Curve results when compared to state-of-the-art models like Graph Neural Networks (GNNs) or the Weisfieler-Lehman graph kernel (WL). Moreover, it outperforms previously proposed hyperdimensional computing graph learning methods. Furthermore, it achieves noteworthy speed enhancements, boasting a 40x acceleration in the training phase and a 15x improvement in inference time compared to GNN and WL models. This not only underscores the efficacy of the HDC-based method, but also highlights its potential for expedited and resource-efficient graph learning.

Related papers

• GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation [13.317620250521124]
  Source-free domain adaptation is a crucial machine learning topic, as it contains numerous applications in the real world. Recent graph neural network (GNN) approaches can suffer from serious performance decline due to domain shift and label scarcity. We propose a novel method named Graph Diffusion-based Alignment with Jigsaw (GALA), tailored for source-free graph domain adaptation.
  arXiv  Detail & Related papers  (2024-10-22T01:32:46Z)
• Gradformer: Graph Transformer with Exponential Decay [69.50738015412189]
  Self-attention mechanism in Graph Transformers (GTs) overlooks the graph's inductive biases, particularly biases related to structure. This paper presents Gradformer, a method innovatively integrating GT with the intrinsic inductive bias. Gradformer consistently outperforms the Graph Neural Network and GT baseline models in various graph classification and regression tasks.
  arXiv  Detail & Related papers  (2024-04-24T08:37:13Z)
• Application of Graph Neural Networks and graph descriptors for graph classification [0.0]
  We focus on Graph Neural Networks (GNNs), which emerged as a de facto standard deep learning technique for graph representation learning. We design fair evaluation experimental protocol and choose proper datasets collection. We arrive to many conclusions, which shed new light on performance and quality of novel algorithms.
  arXiv  Detail & Related papers  (2022-11-07T16:25:22Z)
• GraphHD: Efficient graph classification using hyperdimensional computing [58.720142291102135]
  We present a baseline approach for graph classification with HDC. We evaluate GraphHD on real-world graph classification problems. Our results show that when compared to the state-of-the-art Graph Neural Networks (GNNs) the proposed model achieves comparable accuracy.
  arXiv  Detail & Related papers  (2022-05-16T17:32:58Z)
• A Survey on Graph Representation Learning Methods [7.081604594416337]
  The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. Two of the most prevalent categories of graph representation learning are graph embedding methods without using graph neural nets (GNN) and graph neural nets (GNN) based methods.
  arXiv  Detail & Related papers  (2022-04-04T21:18:48Z)
• Graph Kernel Neural Networks [53.91024360329517]
  We propose to use graph kernels, i.e. kernel functions that compute an inner product on graphs, to extend the standard convolution operator to the graph domain. This allows us to define an entirely structural model that does not require computing the embedding of the input graph. Our architecture allows to plug-in any type of graph kernels and has the added benefit of providing some interpretability.
  arXiv  Detail & Related papers  (2021-12-14T14:48:08Z)
• Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning [18.423192209359158]
  Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks. This paper proposes a hierarchical graph-level representation learning framework, which is adaptively sensitive to graph structures.
  arXiv  Detail & Related papers  (2021-04-13T06:22:24Z)
• Robust Optimization as Data Augmentation for Large-scale Graphs [117.2376815614148]
  We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training. FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks.
  arXiv  Detail & Related papers  (2020-10-19T21:51:47Z)
• Dirichlet Graph Variational Autoencoder [65.94744123832338]
  We present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors. Motivated by the low pass characteristics in balanced graph cut, we propose a new variant of GNN named Heatts to encode the input graph into cluster memberships.
  arXiv  Detail & Related papers  (2020-10-09T07:35:26Z)

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.