Sorry if this is a newbee question, I'm not an expert in data science, so this is the problem:
We have a directed and weighted graph, which higher or lower weight values does not imply the importance of the edge (so preferably the embedding algorithm shouldn't consider higher weights as more important), they are just used to imply the timing of the events which connect the nodes, so the higher weighted edges are events that have happened after the lower ones.
There can be multiple edges between nodes, and I want to do a binary classification using deep learning, meaning that my model gets a embedded graph vector as input and decides if its malicious(1) or not(0).
So what is the best state of the art graph embedding algorithm for this task that can capture as much information as possible from the graph? i read some graph embedding papers but couldn't find any good comparison of them since there are so many new ones.
IMPORTANT NOTE: One problem i have seen with some of the graph embedding algorithms, is that they try to have a small vector dimension, since i guess they are used in fields which there are a LOT of nodes so they need to do this, but in this task its not really important, the nodes are the functions in the program, and they very rarely go above 2000 functions, so even if the algorithm creates a 20k dimensions its no problem, I'm saying this because some of the algorithms that I'm reading will produce a vector that even has lower dimensions compared to number of the nodes in the graph! and that causes loss of information in my opinion. so to sum up, the performance and large vector size is not a problem in my task. so preferably the algorithm should gather as much information as possible from the graph.