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Found 21 papers, 0 papers with code
Knowledge Probing for Graph Representation Learning
Based on the intrinsic properties of graphs, we design three probes to systematically investigate the graph representation learning process from different perspectives, respectively the node-wise level, the path-wise level, and the structural level.
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs.
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
In this paper, we propose a new hierarchical pooling operation, namely the Edge-Node Attention-based Differentiable Pooling (ENADPool), for GNNs to learn effective graph representations.
Dual-modal Prior Semantic Guided Infrared and Visible Image Fusion for Intelligent Transportation System
Therefore, we propose a novel prior semantic guided image fusion method based on the dual-modality strategy, improving the performance of IVF in ITS.
SSHPool: The Separated Subgraph-based Hierarchical Pooling
We commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs.
AKBR: Learning Adaptive Kernel-based Representations for Graph Classification
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification.
AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks
For pairwise graphs, the proposed AERK kernel is defined by computing a reproducing kernel based similarity between the quantum Shannon entropies of their each pair of aligned vertices.
QESK: Quantum-based Entropic Subtree Kernels for Graph Classification
In this paper, we propose a novel graph kernel, namely the Quantum-based Entropic Subtree Kernel (QESK), for Graph Classification.
HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification
In this work, we propose a family of novel quantum kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed graphs.
Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus
To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations.
Writing Style Aware Document-level Event Extraction
This verifies the writing style contains valuable information that could improve the performance of the event extraction task.
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
First, it incorporates the locational correspondence information between graphs into the kernel computation, and thus overcomes the shortcoming of ignoring structural correspondences arising in most R-convolution kernels.
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks.
Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis
With the dominant entropy time series for each pair of financial networks to hand, we develop a similarity measure based on the classical dynamic time warping framework, for analyzing the financial time-varying networks.
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking
During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously.
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification
In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification.
Fused Lasso for Feature Selection using Structural Information
To overcome these issues, we propose a new feature selection method using structural correlation between pairwise samples.
Learning Vertex Convolutional Networks for Graph Classification
In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification.
Identifying The Most Informative Features Using A Structurally Interacting Elastic Net
With the feature graphs to hand, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature graph representation.
Graph Convolutional Neural Networks based on Quantum Vertex Saliency
This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes.
