GNN-IR: Examining graph neural networks for influencer recommendations in social media marketing
With the notable growth of the Internet, a number of platforms have emerged and attracted
an enormous number of users. Based on the impact of these platforms, some 'influencers' …
an enormous number of users. Based on the impact of these platforms, some 'influencers' …
Node classification oriented Adaptive Multichannel Heterogeneous Graph Neural Network
Y Li, C Jian, G Zang, C Song, X Yuan - Knowledge-Based Systems, 2024 - Elsevier
Heterogeneous graph neural networks (HGNNs) play an important role in accomplishing
node classification on heterogeneous graphs (HGs). These models are built on the …
node classification on heterogeneous graphs (HGs). These models are built on the …
Few-shot causal representation learning for out-of-distribution generalization on heterogeneous graphs
Heterogeneous graph few-shot learning (HGFL) has been developed to address the label
sparsity issue in heterogeneous graphs (HGs), which consist of various types of nodes and …
sparsity issue in heterogeneous graphs (HGs), which consist of various types of nodes and …
Long-range Meta-path Search on Large-scale Heterogeneous Graphs
C Li, Z Guo, Q He, K He - The Thirty-eighth Annual Conference on …, 2024 - openreview.net
Utilizing long-range dependency, a concept extensively studied in homogeneous graphs,
remains underexplored in heterogeneous graphs, especially on large ones, posing two …
remains underexplored in heterogeneous graphs, especially on large ones, posing two …
Road Network Representation Learning with the Third Law of Geography
Road network representation learning aims to learn compressed and effective vectorized
representations for road segments that are applicable to numerous tasks. In this paper, we …
representations for road segments that are applicable to numerous tasks. In this paper, we …
Type-adaptive graph Transformer for heterogeneous information networks
Y Tang, Y Huang, J Hou, Z Liu - Applied Intelligence, 2024 - Springer
Many real-world applications use diverse types of nodes and edges to retain rich semantic
information. These applications are modeled as heterogeneous graphs. Recent research on …
information. These applications are modeled as heterogeneous graphs. Recent research on …
Observation impact explanation in atmospheric state estimation using hierarchical message-passing graph neural networks
The impact of meteorological observations on weather forecasting varies with the sensor
type, location, time, and other environmental factors. Thus, the quantitative analysis of …
type, location, time, and other environmental factors. Thus, the quantitative analysis of …
Heterogeneous Sheaf Neural Networks
Heterogeneous graphs, with nodes and edges of different types, are commonly used to
model relational structures in many real-world applications. Standard Graph Neural …
model relational structures in many real-world applications. Standard Graph Neural …
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach
H Gao, C Zhang, F Wu, J Zhao, C Zheng… - arXiv preprint arXiv …, 2024 - arxiv.org
Graph representation learning methods are highly effective in handling complex non-
Euclidean data by capturing intricate relationships and features within graph structures …
Euclidean data by capturing intricate relationships and features within graph structures …
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning
X Zhang, C Xie, H Duan, B Yu - arXiv preprint arXiv:2412.18267, 2024 - arxiv.org
Real-world graph data environments intrinsically exist noise (eg, link and structure errors)
that inevitably disturb the effectiveness of graph representation and downstream learning …
that inevitably disturb the effectiveness of graph representation and downstream learning …