The knowledge graph, which utilizes graph structure to represent multi-relational data, has been widely used in the reasoning and prediction tasks, attracting considerable research efforts recently. However, most existing works still concentrate on learning embeddings straightforwardly intuitively without subtly considering context of knowledge. Specifically, recent models deal with each single...

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the edges between nodes, existing methods mostly learn two embedding vectors each node, source vector and target vector. However, these separately. For node with very low indegree or outdegree, corresponding cannot be effectively learned. In this paper, we propose a novel D...

The target of graph embedding is to embed graphs in vector space such that the embedded feature vectors follow differences and similarities source graphs. In this paper, a novel method named Frequency Filtering Embedding (FFE) proposed which uses Fourier transform filtering as domain operator for extraction. amplifies or attenuates selected frequencies using appropriate filter functions. Here, ...

spectral-based subspace learning is a common data preprocessing step in many machine pipelines. The main aim to learn meaningful low dimensional embedding of the data. However, most methods do not take into consideration possible measurement inaccuracies or artifacts that can lead with high uncertainty. Thus, directly from raw be misleading and negatively impact accuracy. In this paper, we prop...

Some of the parameters used to analyze the efficiency of an embedding are dilation, expansion, edge congestion and wirelength. If e = (u, v) ∈E (G), then the length of Pf (e) in H is called the dilation of the edge e. The maximal dilation over all edges of G is called the dilation of the embedding f. The dilation of embedding G into H is the minimum dilation taken over all embeddings f of G int...