carlyboy
Show HN: A Collection of Monte Carlo Tree Search Papers
Show HN: Edge Enhancement Approach for Motif Aware Community Detection, KDD 2019
Show HN: A collection of graph classification methods (1700 stars)
Show HN: Community Preserving Network Embedding (TensorFlow), AAAI 17
Show HN: Learning Graph Representations with Global Structural Information WWW15
Show HN: Enhanced Network Embedding with Text Information (ICPR 2018)
Show HN: Graph Embedding with Self-Clustering (TensorFlow), Asonam 2019
Re:Show HN: A PyTorch Implementation of Cluster-GCN (KDD 19)
on Monday June 24, 2019 @06:31AM (#20262028)
Nice thing: The results are reproducible regarding accuracy and performance.
Re:Show HN: A PyTorch Implementation of Cluster-GCN (KDD 19)
on Monday June 24, 2019 @06:31AM (#20262025)
Show HN: A PyTorch Implementation of Cluster-GCN (KDD 19)
A collection of network embedding papers with implementations (1600 stars)
Re:Show HN: Learning Structural Node Embeddings via Diffusion Wavelets...
on Saturday June 08, 2019 @06:27AM (#20131683)
Show HN: Learning Structural Node Embeddings via Diffusion Wavelets (KDD 18)
Re:Show HN: Ego-Splitting: From Non-Overlapping to Overlapping Cluster...
on Thursday June 06, 2019 @08:00AM (#20113640)
A video explaining the paper:
https://www.youtube.com/watch?v=xMGZo-F_jss
The paper itself:
https://www.eecs.yorku.ca/course_archive/2017-18/F/6412/read...
Show HN: Ego-Splitting: From Non-Overlapping to Overlapping Clusters (KDD 17)
Show HN: Attributed Social Network Embedding (TKDE 2018), TensorFlow
Show HN: Predict Then Propagate, ICLR 2019 (PyTorch)
Re:A collection of graph classification procedures with implementations
on Saturday June 01, 2019 @05:18PM (#20071759)
The collection covers these types of methods exhaustively:
1. Factorization
2. Spectral and Statistical Fingerprints
3. Deep Learning
4. Graph Kernels
A collection of graph classification procedures with implementations
Show HN: Learning Node Representations That Capture Multiple Contexts, WWW 2019
Show HN: Sine: Scalable Incomplete Network Embedding (ICDM 2018)
Show HN: Deep Autoencoder-Like Nonnegative Matrix Factorization, CIKM 2018
Show HN: MixHop: Higher-Order Graph Convolutional Architectures, ICML 2019
Re:A collection of fraud detection research papers
on Wednesday May 29, 2019 @03:53AM (#20037707)
A curated list of papers about fraud detection from the following conferences:
Network Science
ASONAM
Natural Language Processing
ACL
Data Mining
KDD
ICDM
SDM
WWW
Artificial Intelligence
AAAI
AISTATS
IJCAI
UAIA collection of fraud detection research papers
Re:Gradient and adaptive boosting research papers from the last 25 years
on Saturday May 25, 2019 @10:17AM (#20009188)
A curated list of gradient boosting papers with implementations from the following conferences:
Machine learning:
1. NeurIPS
2. ICML
3. ICLR
1. CVPR
2. ICCV
3. ECCV
1. ACL
2. NAACL
3. EMNLP
1. KDD
2. ICDM
3. CIKM
4. WWW
1. AAAI
2. IJCAI
3. UAI
4. AISTATSGradient and adaptive boosting research papers from the last 25 years
Show HN: A lightwieght sparse SciPy implementation of GraRep
Re:Show HN: Role2Vec: Learning Role-Based Graph Embeddings, Ijcai 2018
on Friday May 24, 2019 @04:36AM (#19999625)
The reasons that I had for implementing the paper (things that I loved about the paper):
1. Compared to other structural node embedding techniques it is fast.
2. It was easy to build it on high performance implicit factorization packages (e.g. gensim).
3. The structural features are learned from motifs.
4. Structural properties of neighbors are also included.
5. The paper has some theoretical aesthetics.