Open TruongDuyLongPTIT opened 2 years ago
Hi there! Here are some clarifications
e = (A, B)
has two end nodes, one as "source node" A
and one as "destination node" B
. These two nodes are usually different (except self-loops when A = B
). So here we just mean to use the degree of the source node A
, instead of that of B
. This applies to both undirected and directed graphs (in undirected graphs, it is just the degree; in directed graphs, this could be in-degree or out-degree, in which we use would out-degree).messages
(hidden vectors from neighbors) received to a node B
at some layer l
, can be maintained at the next layer l + 1
when a new round of message aggregation happens. This is because the current information at node B
will be passed to itself through self-loop. It is a convenient way of keeping the information by adding the self-loops in data without changing the model logic.Hope this helps!
Thanks. You havel me a lot.
Hi, i need your help. 1) When i read your paper. I saw you said: "All the graphs are undirected and preprocessed to have self-loops to speed up training". Besides, you also can said: "we propose to use a random walk style normalization ̄A=D−1A which only involves the degree of the source nodes to equate the normalized adjacency matrix to the corresponding probability transition matrix". In here, you use "degree of source node" terms, i think this terms equivalent with "out-degree" terms. But "out-degree" terms only use for directed graph. So, it make me confuse, i can't understand your graph is undirected or directed. 2) Why self-loop can speed up trainning? and What mean "normalized adjacency matrix to the corresponding probability transition matrix"? 3) I see your code in botgen folder. It seem create a botnet by pick random some node. So, I want to ask, by randomly selecting bot nodes, is it possible to create a botnet with the same topology as in reality and why? Thank for your help!