to anomaly detection on attributed graphs. Extensive experiments on eight real-life graph datasets demonstrate the effectiveness of ComGA. The toy example is a Twitter graph with different types of ...

Abstract: Graph representation learning aims at mapping a graph into a lower-dimensional feature space. Deep attributed graph representation, utilizing deep learning models on the graph structure and ...

Abstract: Attributed graphs are widely used in many application domains, for example to model social networks. An attributed graph is a graph in which vertices and edges may have types and other ...

For example, if a user gets more retweets from “music ... There are 407,533 vertices, 2, 209, 169 edges, and 2, 727, 464 keywords in this attributed graph. To generate a query for OAG dataset, first, ...

Below is an example of the pipeline used to generate the graphs from ... The former may be interested in the Attribute Graphs whereas the latter may be interested in the Embedded and Explained Graphs, ...

The classification of a given node in the attributed multiplex network must consider ... graph networks but can naturally model using multiplex graph networks to describe different links. For example, ...

For example, some equipment attribute specifications are incompletely filled in ... By constructing a UHV knowledge graph, unstructured historical engineering data can be stored as nodes and relations ...

The growing sizes of graph databases motivate the crucial need for developing powerful and scalable graphbased query engines. We propose a SPARQL-like language, G-SPARQL, for querying attributed ...