thudm / mcns Goto Github PK
View Code? Open in Web Editor NEWSource code and dataset for KDD 2020 paper "Understanding Negative Sampling in Graph Representation Learning"
Source code and dataset for KDD 2020 paper "Understanding Negative Sampling in Graph Representation Learning"
hi authors,
this work is difficult to me to understand, do you have slides or video including more details ?
Is there also example code for the node classification task as reported in the paper?
Thank you.
There was a define about central node in figure 2 said "The central nodes are traversed by DFS", but I can't see define of node1 by dfs in code, and only see dfs used to sample next node in MH sampler.
Looking forward to your reply, please point out if there is any problem with my understanding
Hi,
Is there an example of the link prediction task?
Hello,
I am currently studying negative sampling in the recommendation systems field. In your paper, you compared the current negative sampling methods and your proposed method. In your repo, I could not see the state-of-the-art methods' functions. How can I access the state-of-the-art methods' codes that you mentioned in your paper? Can you help me with this?
Thanks in advance.
请教一下, 在 deepwalk 推荐任务代码实现中, 计算 loss 或 accuracy 时, 为什么负样本采样个数为 1 呢?
实际中不是应该设置为 k 么
def _loss(self):
aff = tf.reduce_sum(tf.multiply(self.outputs1, self.outputs2), 1) + self.true_b
neg_aff = tf.reduce_sum(tf.multiply(self.outputs1, self.neg_outputs), 1) + self.neg_b
# xent_loss
true_xent = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(aff), logits=aff)
negative_xent = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(neg_aff), logits=neg_aff)
loss = tf.reduce_sum(true_xent) + tf.reduce_sum(negative_xent)
self.loss = loss / tf.cast(self.batch_size, tf.float32)
self.merged_loss = tf.summary.scalar('merged_loss', self.loss)
Hi,author
Whether the argument, 'user_num' and 'item_num', in recommendation need to be pre-defined?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.