Question about sampling methods in minibatch about graphsage HOT 3 CLOSED

-- 发自我的网易邮箱手机智能版 在 2017-10-10 03:42:34，"William L Hamilton" <[email protected]> 写道： Hi, Yes this can be confusing/counterintuitive and has to do with the naming conventions we use, as well how we "unfold" the recursion/loop of Algorithm 1 in our paper. We are making considerable efforts to clarify this in the next version of our paper, as well in updates to the code. The short answer is that this is just a convention, which is complicated by the fact that the minibatch sampling is conceptually reversed compared to the outer loop (over "k") in Algorithm 1 (in our paper). By convention, when we refer to "layer k" in the code, we (usually) mean the computations/nodes present at "depth k" in Algorithm 1. But the key thing to note is that the sampling in our minibatch algorithm is in reverse order compared to outer loop (iteration over k) in Algorithm 1. In particular, say we want to generate embeddings for a set of input, or target, nodes in a minibatch with depth K=2 and with neighborhood sample sizes S_1=10 and S_2=25 (i.e., we are using information from 2-hop neighborhoods), then, in our current convention, "layer 0" nodes (corresponding to the k=0 base case in Algorithm 1) are the nodes that are 2-hops away from the target nodes (and we need 10*25 of them per input node), "layer-1" nodes are 1-hop away (and we need 10 of them per input node), and "layer-2" nodes are target/input nodes. Put in another way, the representations we want to generate -- for the input/target nodes -- are at depth/layer k=2 and these representations recursively depend on representations of the input nodes's neighbors at the previous layers. So from the perspective of sampling, we must sample the higher-depth/layer nodes first. Anyways, I hope this made some sense, as this was a somewhat rushed reply. In any event, we will aim to clarify this in much more detail in updates to come. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub, or mute the thread.