AdaGNN: A multi-modal latent representation meta-learner for GNNs based
on AdaBoosting
- URL: http://arxiv.org/abs/2108.06452v1
- Date: Sat, 14 Aug 2021 03:07:26 GMT
- Title: AdaGNN: A multi-modal latent representation meta-learner for GNNs based
on AdaBoosting
- Authors: Qinyi Zhu, Yiou Xiao
- Abstract summary: Graph Neural Networks (GNNs) focus on extracting intrinsic network features.
We propose boosting-based meta learner for GNNs.
AdaGNN performs exceptionally well for applications with rich and diverse node neighborhood information.
- Score: 0.38073142980733
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As a special field in deep learning, Graph Neural Networks (GNNs) focus on
extracting intrinsic network features and have drawn unprecedented popularity
in both academia and industry. Most of the state-of-the-art GNN models offer
expressive, robust, scalable and inductive solutions empowering social network
recommender systems with rich network features that are computationally
difficult to leverage with graph traversal based methods.
Most recent GNNs follow an encoder-decoder paradigm to encode high
dimensional heterogeneous information from a subgraph onto one low dimensional
embedding space. However, one single embedding space usually fails to capture
all aspects of graph signals. In this work, we propose boosting-based meta
learner for GNNs, which automatically learns multiple projections and the
corresponding embedding spaces that captures different aspects of the graph
signals. As a result, similarities between sub-graphs are quantified by
embedding proximity on multiple embedding spaces. AdaGNN performs exceptionally
well for applications with rich and diverse node neighborhood information.
Moreover, AdaGNN is compatible with any inductive GNNs for both node-level and
edge-level tasks.
Related papers
- GNN-Ensemble: Towards Random Decision Graph Neural Networks [3.7620848582312405]
Graph Neural Networks (GNNs) have enjoyed wide spread applications in graph-structured data.
GNNs are required to learn latent patterns from a limited amount of training data to perform inferences on a vast amount of test data.
In this paper, we push one step forward on the ensemble learning of GNNs with improved accuracy, robustness, and adversarial attacks.
arXiv Detail & Related papers (2023-03-20T18:24:01Z) - Higher-order Sparse Convolutions in Graph Neural Networks [17.647346486710514]
We introduce a new higher-order sparse convolution based on the Sobolev norm of graph signals.
S-SobGNN shows competitive performance in all applications as compared to several state-of-the-art methods.
arXiv Detail & Related papers (2023-02-21T08:08:18Z) - Geodesic Graph Neural Network for Efficient Graph Representation
Learning [34.047527874184134]
We propose an efficient GNN framework called Geodesic GNN (GDGNN)
It injects conditional relationships between nodes into the model without labeling.
Conditioned on the geodesic representations, GDGNN is able to generate node, link, and graph representations that carry much richer structural information than plain GNNs.
arXiv Detail & Related papers (2022-10-06T02:02:35Z) - ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network [72.16255675586089]
We propose an Adaptive Curvature Exploration Hyperbolic Graph NeuralNetwork named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks.
Experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.
arXiv Detail & Related papers (2021-10-15T07:18:57Z) - IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking
Systems [4.1591055164123665]
We present IGNNITION, a novel open-source framework that enables fast prototyping of Graph Neural Networks (GNNs) for networking systems.
IGNNITION is based on an intuitive high-level abstraction that hides the complexity behind GNNs.
Our results show that the GNN models produced by IGNNITION are equivalent in terms of accuracy and performance to their native implementations.
arXiv Detail & Related papers (2021-09-14T14:28:21Z) - Identity-aware Graph Neural Networks [63.6952975763946]
We develop a class of message passing Graph Neural Networks (ID-GNNs) with greater expressive power than the 1-WL test.
ID-GNN extends existing GNN architectures by inductively considering nodes' identities during message passing.
We show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy improvement on challenging node, edge, and graph property prediction tasks.
arXiv Detail & Related papers (2021-01-25T18:59:01Z) - A Unified View on Graph Neural Networks as Graph Signal Denoising [49.980783124401555]
Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data.
In this work, we establish mathematically that the aggregation processes in a group of representative GNN models can be regarded as solving a graph denoising problem.
We instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes.
arXiv Detail & Related papers (2020-10-05T04:57:18Z) - Distance Encoding: Design Provably More Powerful Neural Networks for
Graph Representation Learning [63.97983530843762]
Graph Neural Networks (GNNs) have achieved great success in graph representation learning.
GNNs generate identical representations for graph substructures that may in fact be very different.
More powerful GNNs, proposed recently by mimicking higher-order tests, are inefficient as they cannot sparsity of underlying graph structure.
We propose Distance Depiction (DE) as a new class of graph representation learning.
arXiv Detail & Related papers (2020-08-31T23:15:40Z) - Graph Neural Networks: Architectures, Stability and Transferability [176.3960927323358]
Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs.
They are generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters.
arXiv Detail & Related papers (2020-08-04T18:57:36Z) - A Collective Learning Framework to Boost GNN Expressiveness [25.394456460032625]
We consider the task of inductive node classification using Graph Neural Networks (GNNs) in supervised and semi-supervised settings.
We propose a general collective learning approach to increase the representation power of any existing GNN.
We evaluate performance on five real-world network datasets and demonstrate consistent, significant improvement in node classification accuracy.
arXiv Detail & Related papers (2020-03-26T22:07:28Z) - EdgeNets:Edge Varying Graph Neural Networks [179.99395949679547]
This paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet.
An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors.
This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs)
arXiv Detail & Related papers (2020-01-21T15:51:17Z)
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.