Graphs are a ubiquitous representation and powerful data structure that ... such as network embeddings and representation learning, have led to unprecedented progress in solving many challenges facing ...

In this project we will study a social network and graphs of users’ personal friendship network. We will explore community structures in the friendship network and ...

the questions I will investigate and the algorithms and data structures I'll use to investigate these questions, including an analysis of the appropriateness and limitations of these algorithms.

When we apply a graph neural network to the time series data, we call it the Spatio ... an adjacency sparse matrix that defines a graph structure in forwarding processing. The following are the main ...

Abstract: Graph is a commonly used data structure to store large relational data in today's education networks. With the growing demand for storing and processing large graph data, graph data ...

Abstract: In this paper, we present a novel Siamese graph convolution network (GCN) for face sketch recognition ... It is also shown that the model performance based on the graph structure ...

Graph structured data ... network models. These GNNs offer effective means to acquire representations at both the node and graph levels. The commendable representation learning ability of GNNs has led ...