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Found 9 papers, 8 papers with code
Automatic Question-Answer Generation for Long-Tail Knowledge
Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA).
Multimodal Graph Learning for Generative Tasks
We study three research questions raised by MMGL: (1) how can we infuse multiple neighbor information into the pretrained LMs, while avoiding scalability issues?
Graph Generative Model for Benchmarking Graph Neural Networks
As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems.
A Dataset on Malicious Paper Bidding in Peer Review
The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks
We propose a zero-shot transfer learning module for HGNNs called a Knowledge Transfer Network (KTN) that transfers knowledge from label-abundant node types to zero-labeled node types through rich relational information given in the HG.
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
This allows us to detect sudden changes in the importance of any node.
Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off
We first define a unified framework UNIFIEDGM that integrates various message-passing based graph algorithms, ranging from conventional algorithms like PageRank to graph neural networks.
Real-Time Anomaly Detection in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?
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Anomaly Detection in Edge Streams
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