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Found 6 papers, 4 papers with code
Hierarchical Attention Models for Multi-Relational Graphs
BR-GCN models use bi-level attention to learn node embeddings through (1) node-level attention, and (2) relation-level attention.
Bi-Level Attention Graph Neural Networks
Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs).
Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs
For works that seek to put both views of the KG together, the instance and ontology views are assumed to belong to the same geometric space, such as all nodes embedded in the same Euclidean space or non-Euclidean product space, an assumption no longer reasonable for two-view KGs where different portions of the graph exhibit different structures.
Graph-based Molecular Representation Learning
Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.
Question-Answer Sentence Graph for Joint Modeling Answer Selection
This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems.
Software Language Comprehension using a Program-Derived Semantics Graph
To continue to advance this research, we present the program-derived semantics graph, a new graphical structure to capture semantics of code.
