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Found 8 papers, 4 papers with code
Friend Ranking in Online Games via Pre-training Edge Transformers
Friend recall is an important way to improve Daily Active Users (DAU) in online games.
SCR: Training Graph Neural Networks with Consistency Regularization
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
Ranked #3 on
Node Property Prediction
on ogbn-papers100M
Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces.
Initialization for Network Embedding: A Graph Partition Approach
Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification.
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
Graph is an important data representation which appears in a wide diversity of real-world scenarios.
Active Learning for Graph Embedding
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information.
From Community Detection to Community Profiling
Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links.
From Node Embedding To Community Embedding
Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space.
