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Found 14 papers, 8 papers with code
KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web.
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding.
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously.
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection
Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL).
NameGuess: Column Name Expansion for Tabular Data
Recent advances in large language models have revolutionized many sectors, including the database industry.
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
Recent advances in tabular data generation have greatly enhanced synthetic data quality.
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction
However, GNN explanation for link prediction (LP) is lacking in the literature.
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.
Ranked #1 on
Node Clustering
on IMDb
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario.
Title-Guided Encoding for Keyphrase Generation
Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP).
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network
However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect.
Dynamic Key-Value Memory Networks for Knowledge Tracing
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities.
