Multi-Label Graph Convolutional Network Representation Learning
- URL: http://arxiv.org/abs/1912.11757v1
- Date: Thu, 26 Dec 2019 02:52:47 GMT
- Title: Multi-Label Graph Convolutional Network Representation Learning
- Authors: Min Shi, Yufei Tang, Xingquan Zhu and Jianxun Liu
- Abstract summary: We propose a novel multi-label graph convolutional network (ML-GCN) for learning node representation for multi-label networks.
The two GCNs each handle one aspect of representation learning for nodes and labels, respectively, and they are seamlessly integrated under one objective function.
- Score: 20.059242373860013
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Knowledge representation of graph-based systems is fundamental across many
disciplines. To date, most existing methods for representation learning
primarily focus on networks with simplex labels, yet real-world objects (nodes)
are inherently complex in nature and often contain rich semantics or labels,
e.g., a user may belong to diverse interest groups of a social network,
resulting in multi-label networks for many applications. The multi-label
network nodes not only have multiple labels for each node, such labels are
often highly correlated making existing methods ineffective or fail to handle
such correlation for node representation learning. In this paper, we propose a
novel multi-label graph convolutional network (ML-GCN) for learning node
representation for multi-label networks. To fully explore label-label
correlation and network topology structures, we propose to model a multi-label
network as two Siamese GCNs: a node-node-label graph and a label-label-node
graph. The two GCNs each handle one aspect of representation learning for nodes
and labels, respectively, and they are seamlessly integrated under one
objective function. The learned label representations can effectively preserve
the inner-label interaction and node label properties, and are then aggregated
to enhance the node representation learning under a unified training framework.
Experiments and comparisons on multi-label node classification validate the
effectiveness of our proposed approach.
Related papers
- Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification [32.4968073593255]
We propose a Correlation-aware Graph Convolutional Network (CorGCN) for multi-label node classification.
By introducing a novel Correlation-Aware Graph Decomposition module, CorGCN can learn a graph that contains rich label-correlated information for each label.
arXiv Detail & Related papers (2024-11-26T11:52:47Z) - A data-centric approach for assessing progress of Graph Neural Networks [7.2249434861826325]
Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks.
Most improvements are in multi-class classification, with less focus on the cases where each node could have multiple labels.
First challenge in studying multi-label node classification is the scarcity of publicly available datasets.
arXiv Detail & Related papers (2024-06-18T09:41:40Z) - KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot
Node Classification [75.95647590619929]
Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis.
We propose a Knowledge-Aware Multi-Faceted framework (KMF) that enhances the richness of label semantics.
A novel geometric constraint is developed to alleviate the problem of prototype drift caused by node information aggregation.
arXiv Detail & Related papers (2023-08-15T02:38:08Z) - Contrastive Meta-Learning for Few-shot Node Classification [54.36506013228169]
Few-shot node classification aims to predict labels for nodes on graphs with only limited labeled nodes as references.
We create a novel contrastive meta-learning framework on graphs, named COSMIC, with two key designs.
arXiv Detail & Related papers (2023-06-27T02:22:45Z) - Label-Enhanced Graph Neural Network for Semi-supervised Node
Classification [32.64730237473914]
We present a label-enhanced learning framework for Graph Neural Networks (GNNs)
It first models each label as a virtual center for intra-class nodes and then jointly learns the representations of both nodes and labels.
Our approach could not only smooth the representations of nodes belonging to the same class, but also explicitly encode the label semantics into the learning process of GNNs.
arXiv Detail & Related papers (2022-05-31T09:48:47Z) - Graph Attention Transformer Network for Multi-Label Image Classification [50.0297353509294]
We propose a general framework for multi-label image classification that can effectively mine complex inter-label relationships.
Our proposed methods can achieve state-of-the-art performance on three datasets.
arXiv Detail & Related papers (2022-03-08T12:39:05Z) - Dynamic Labeling for Unlabeled Graph Neural Networks [34.65037955481084]
Graph neural networks (GNNs) rely on node embeddings to represent a node as a vector by its identity, type, or content.
Existing GNNs either assign random labels to nodes or assign one embedding to all nodes, which fails to distinguish one node from another.
In this paper, we analyze the limitation of existing approaches in two types of classification tasks, graph classification and node classification.
arXiv Detail & Related papers (2021-02-23T04:30:35Z) - Knowledge-Guided Multi-Label Few-Shot Learning for General Image
Recognition [75.44233392355711]
KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks.
It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence.
Then, it introduces the label semantics to guide learning semantic-specific features.
It exploits a graph propagation network to explore graph node interactions.
arXiv Detail & Related papers (2020-09-20T15:05:29Z) - Inverse Graph Identification: Can We Identify Node Labels Given Graph
Labels? [89.13567439679709]
Graph Identification (GI) has long been researched in graph learning and is essential in certain applications.
This paper defines a novel problem dubbed Inverse Graph Identification (IGI)
We propose a simple yet effective method that makes the node-level message passing process using Graph Attention Network (GAT) under the protocol of GI.
arXiv Detail & Related papers (2020-07-12T12:06:17Z) - Sequential Graph Convolutional Network for Active Learning [53.99104862192055]
We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN)
With a small number of randomly sampled images as seed labelled examples, we learn the parameters of the graph to distinguish labelled vs unlabelled nodes.
We exploit these characteristics of GCN to select the unlabelled examples which are sufficiently different from labelled ones.
arXiv Detail & Related papers (2020-06-18T00:55:10Z)
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.