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Found 4 papers, 2 papers with code
ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation
Directed graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e. g., question, answer, user.
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MILE: A Multi-Level Framework for Scalable Graph Embedding
We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs.
Semi-supervised Embedding in Attributed Networks with Outliers
In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN).
Robust Contextual Outlier Detection: Where Context Meets Sparsity
To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD).
