2 research outputs found
Graph-based Deep-Tree Recursive Neural Network (DTRNN) for Text Classification
A novel graph-to-tree conversion mechanism called the deep-tree generation
(DTG) algorithm is first proposed to predict text data represented by graphs.
The DTG method can generate a richer and more accurate representation for nodes
(or vertices) in graphs. It adds flexibility in exploring the vertex
neighborhood information to better reflect the second order proximity and
homophily equivalence in a graph. Then, a Deep-Tree Recursive Neural Network
(DTRNN) method is presented and used to classify vertices that contains text
data in graphs. To demonstrate the effectiveness of the DTRNN method, we apply
it to three real-world graph datasets and show that the DTRNN method
outperforms several state-of-the-art benchmarking methods
Graph Representation Learning: A Survey
Research on graph representation learning has received a lot of attention in
recent years since many data in real-world applications come in form of graphs.
High-dimensional graph data are often in irregular form, which makes them more
difficult to analyze than image/video/audio data defined on regular lattices.
Various graph embedding techniques have been developed to convert the raw graph
data into a low-dimensional vector representation while preserving the
intrinsic graph properties. In this review, we first explain the graph
embedding task and its challenges. Next, we review a wide range of graph
embedding techniques with insights. Then, we evaluate several state-of-the-art
methods against small and large datasets and compare their performance.
Finally, potential applications and future directions are presented