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Found 9 papers, 0 papers with code
Conversational Question Answering with Reformulations over Knowledge Graph
CornNet adopts a teacher-student architecture where a teacher model learns question representations using human writing reformulations, and a student model to mimic the teacher model's output via reformulations generated by LLMs.
Hierarchical Multi-Marginal Optimal Transport for Network Alignment
To depict high-order relationships across multiple networks, the FGW distance is generalized to the multi-marginal setting, based on which networks can be aligned jointly.
Neural Multi-network Diffusion towards Social Recommendation
Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation.
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations.
Optimal Propagation for Graph Neural Networks
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input.
SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation
Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users.
Hypergraph Pre-training with Graph Neural Networks
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting.
Graph Sanitation with Application to Node Classification
Generally speaking, the vast majority of the existing works aim to answer the following question, that is, given a graph, what is the best way to mine it?
KompaRe: A Knowledge Graph Comparative Reasoning System
In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
