benitorosenberg

7 ms·

1.
by benitorosenberg 4y ago
Participated in their competition and won $50,000 - developed an ML system based on their DB and I can tell that their product is completely broken. Does not scale, poorly documented. The whole company felt like a marketing stunt. Would be
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A Git log based CV generator(github.com)
1 points by benitorosenberg 5y ago|0 comments
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Explaianble Graph Machine Learning a Survey(github.com)
1 points by benitorosenberg 5y ago|0 comments
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Show HN: PyTorch Geometric Temporal – Deep learning for dynamic graphs(github.com)
2 points by benitorosenberg 6y ago|0 comments
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by benitorosenberg 6y ago
Sounds like a good idea, might generate a dynamic visualization on some examples. :)
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Show HN: Little Ball of Fur 2.0 – A graph sampling Python library(github.com)
24 points by benitorosenberg 6y ago|3 comments
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A Collection of Fraud Detection Research Papers with Implementations(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: Karate Club – A Python Machine Learning Library for Graphs(github.com)
1 points by benitorosenberg 6y ago|0 comments
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Show HN: Karate Club – A Python library for unsupervised learning on graphs(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: Karate Club – A Python library for machine learning on graphs(github.com)
2 points by benitorosenberg 6y ago|0 comments
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by benitorosenberg 6y ago
The Leskovec papers in the readme are pretty good (classical pieces) I would really recommend reading them.
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by benitorosenberg 6y ago
TY! I fixed it.
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Show HN: Little Ball of Fur – A Python library for graph subsampling(github.com)
119 points by benitorosenberg 6y ago|12 comments
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by benitorosenberg 6y ago
Entire graph embedding means that you have a lot of smaller graphs (e.g. molecules, transactions, threads) and you want to classify them. We created this package which covers these methods: https://github.com/benedekrozember
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by benitorosenberg 6y ago
The reason for not doing that is the bias that such sampling introduces. We are writing a paper out of this, but the main point is that you can achieve these two things with minimal classification performance degradation: 1. Speeding up nod
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Show HN: Little Ball of Fur – A NetworkX extension library for graph sampling(github.com)
16 points by benitorosenberg 6y ago|4 comments
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Show HN: Scalable Incomplete Network Embedding (ICDM 2018)(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: Graph Wavelet Neural Network (PyTorch)(github.com)
4 points by benitorosenberg 6y ago|0 comments
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Show HN: Attributed Social Network Embedding (TensorFlow)(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: Learning Structural Node Embeddings via Diffusion Wavelets(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: Learning Node Representations That Capture Multiple Contexts (PyTorch)(github.com)
3 points by benitorosenberg 6y ago|0 comments
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Show HN: A Neural Network for Graph Similarity Computation WSDM'19 (PyTorch)(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: Role2Vec: Learning Role-Based Graph Embeddings(github.com)
14 points by benitorosenberg 6y ago|0 comments
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Show HN: Ego-Splitting: From Non-Overlapping to Overlapping Clusters(github.com)
5 points by benitorosenberg 6y ago|0 comments
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Show HN: Deep Autoencoder-Like Nonnegative Matrix Factorization(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: Predict Then Propagate Graph Neural Networks Meet Personalized PageRank(github.com)
3 points by benitorosenberg 6y ago|1 comments
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Show HN: Graph classification using structural attention (PyTorch)(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: Graph Embedding with Self-Clustering (TensorFlow), Asonam 2019(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: Signed Graph Convolutional Network (PyTorch)(github.com)
2 points by benitorosenberg 6y ago|0 comments
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Show HN: A Parallel Implementation of Graph2Vec(github.com)
3 points by benitorosenberg 6y ago|0 comments

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