Search Results for author:
Found 7 papers, 3 papers with code
Beyond the Known: Novel Class Discovery for Open-world Graph Learning
Inter-class correlations are subsequently eliminated by the prototypical attention network, leading to distinctive representations for different classes.
DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning
(2) the scarcity of labeled data for rare events, which is a pervasive issue in the medical field where rare yet potentially critical interactions are often overlooked or under-studied due to limited available data.
Prompt Learning on Temporal Interaction Graphs
In detail, we propose a temporal prompt generator to offer temporally-aware prompts for different tasks.
Graph Prompt Learning: A Comprehensive Survey and Beyond
This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications.
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation
To overcome the indiscriminate updating issue, we introduce the Adaptive Short-term Updater module that will automatically discard the useless or noisy edges, ensuring iLoRE's effectiveness and instant ability.
Dual Intents Graph Modeling for User-centric Group Discovery
Therefore, user-centric group discovery task, i. e., recommending groups to users can help both users' online experiences and platforms' long-term developments.
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task.
