264,348 research outputs found
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs
Heterogeneous graph neural networks (HGNNs) have been widely applied in
heterogeneous information network tasks, while most HGNNs suffer from poor
scalability or weak representation when they are applied to large-scale
heterogeneous graphs. To address these problems, we propose a novel
Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning
(RHCO) for large-scale heterogeneous graph representation learning. Unlike
traditional heterogeneous graph neural networks, we adopt the contrastive
learning mechanism to deal with the complex heterogeneity of large-scale
heterogeneous graphs. We first learn relation-aware node embeddings under the
network schema view. Then we propose a novel positive sample selection strategy
to choose meaningful positive samples. After learning node embeddings under the
positive sample graph view, we perform a cross-view contrastive learning to
obtain the final node representations. Moreover, we adopt the label smoothing
technique to boost the performance of RHCO. Extensive experiments on three
large-scale academic heterogeneous graph datasets show that RHCO achieves best
performance over the state-of-the-art models
Subgraph Contrastive Link Representation Learning
Graph representation learning (GRL) has emerged as a powerful technique for
solving graph analytics tasks. It can effectively convert discrete graph data
into a low-dimensional space where the graph structural information and graph
properties are maximumly preserved. While there is rich literature on node and
whole-graph representation learning, GRL for link is relatively less studied
and less understood. One common practice in previous works is to generate link
representations by directly aggregating the representations of their incident
nodes, which is not capable of capturing effective link features. Moreover,
common GRL methods usually rely on full-graph training, suffering from poor
scalability and high resource consumption on large-scale graphs. In this paper,
we design Subgraph Contrastive Link Representation Learning (SCLRL) -- a
self-supervised link embedding framework, which utilizes the strong correlation
between central links and their neighborhood subgraphs to characterize links.
We extract the "link-centric induced subgraphs" as input, with a subgraph-level
contrastive discrimination as pretext task, to learn the intrinsic and
structural link features via subgraph mini-batch training. Extensive
experiments conducted on five datasets demonstrate that SCLRL has significant
performance advantages in link representation learning on benchmark datasets
and prominent efficiency advantages in terms of training speed and memory
consumption on large-scale graphs, when compared with existing link
representation learning methods.Comment: 8 pages, 4 figure
An Effective and Efficient Graph Representation Learning Approach for Big Graphs
In the Big Data era, large graph datasets are becoming increasingly popular due to their capability to integrate and interconnect large sources of data in many fields, e.g., social media, biology, communication networks, etc. Graph representation learning is a flexible tool that automatically extracts features from a graph node. These features can be directly used for machine learning tasks. Graph representation learning approaches producing features preserving the structural information of the graphs are still an open problem, especially in the context of large-scale graphs. In this paper, we propose a new fast and scalable structural representation learning approach called SparseStruct. Our approach uses a sparse internal representation for each node, and we formally proved its ability to preserve structural information. Thanks to a light-weight algorithm where each iteration costs only linear time in the number of the edges, SparseStruct is able to easily process large graphs. In addition, it provides improvements in comparison with state of the art in terms of prediction and classification accuracy by also providing strong robustness to noise data
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information
Since most scientific literature data are unlabeled, this makes unsupervised
graph-based semantic representation learning crucial. Therefore, an
unsupervised semantic representation learning method of scientific literature
based on graph attention mechanism and maximum mutual information (GAMMI) is
proposed. By introducing a graph attention mechanism, the weighted summation of
nearby node features make the weights of adjacent node features entirely depend
on the node features. Depending on the features of the nearby nodes, different
weights can be applied to each node in the graph. Therefore, the correlations
between vertex features can be better integrated into the model. In addition,
an unsupervised graph contrastive learning strategy is proposed to solve the
problem of being unlabeled and scalable on large-scale graphs. By comparing the
mutual information between the positive and negative local node representations
on the latent space and the global graph representation, the graph neural
network can capture both local and global information. Experimental results
demonstrate competitive performance on various node classification benchmarks,
achieving good results and sometimes even surpassing the performance of
supervised learning
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