Planarity game
If you need some time to procrastinate and you want to do it with graphs, here is a fun game to play, called Tronix2. You just need to make the drawn graphs planar. There are several clones of this game (here and here), which even explain how to generate planar graphs. And here is Numberphile video about planar graphs.
If you need some time to procrastinate and you want to do it with graphs, here is a fun game to play, called Tronix2. You just need to make the drawn graphs planar. There are several clones of this game (here and here), which even explain how to generate planar graphs. And here is Numberphile video about planar graphs.
Atom
Treksit.com is for sale at Atom!
Domain name Treksit.com evokes a sense of adventure and exploration, making it ideal for travel, outdoor, or tech startups. The word trek suggests a journey or expedition, while sit implies a place to relax and reflect. This duality can app . " #CatchyDomainsโฆ
Golden Knowledge Graph
Golden is a Silicon Valley startup building a knowledge database (similar to Wikipedia) โ a good example how knowledge graphs can be commercialized.
Golden is a Silicon Valley startup building a knowledge database (similar to Wikipedia) โ a good example how knowledge graphs can be commercialized.
Fresh picks from ArXiv
Today at ArXiv: fast algorithms for dynamic graphs, new adversarial attacks, and GNNs for generating dances ๐
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNN
- Curvature Regularization to Prevent Distortion in Graph Embedding NeurIPS 2020
- Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules with Stephan Gรผnnemann, Workshop NeurIPS 2020
- A Targeted Universal Attack on Graph Convolutional Network
- Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach with Christos Faloutsos
- Near-Optimal Algorithms for Reachability, Strongly-Connected Components and Shortest Paths in Partially Dynamic Digraphs
Applications
- Video Self-Stitching Graph Network for Temporal Action Localization
- Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio
- RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design Workshop NeurIPS 2020
- Quantifying Explainers of Graph Neural Networks in Computational Pathology
Today at ArXiv: fast algorithms for dynamic graphs, new adversarial attacks, and GNNs for generating dances ๐
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNN
- Curvature Regularization to Prevent Distortion in Graph Embedding NeurIPS 2020
- Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules with Stephan Gรผnnemann, Workshop NeurIPS 2020
- A Targeted Universal Attack on Graph Convolutional Network
- Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach with Christos Faloutsos
- Near-Optimal Algorithms for Reachability, Strongly-Connected Components and Shortest Paths in Partially Dynamic Digraphs
Applications
- Video Self-Stitching Graph Network for Temporal Action Localization
- Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio
- RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design Workshop NeurIPS 2020
- Quantifying Explainers of Graph Neural Networks in Computational Pathology
Undergraduate Math Student Pushes Frontier of Graph Theory
A new article at QuantaMagazine about 21 year old who improved results of Erdลs and Szekeres on the upper bound for two-color Ramsey numbers. Informally, Ramsey numbers can be explained as "how big graphs can get before patterns inevitably emerge". This is in addition to the recent proof for lower bounds, also covered in Quanta.
A new article at QuantaMagazine about 21 year old who improved results of Erdลs and Szekeres on the upper bound for two-color Ramsey numbers. Informally, Ramsey numbers can be explained as "how big graphs can get before patterns inevitably emerge". This is in addition to the recent proof for lower bounds, also covered in Quanta.
Quanta Magazine
Undergraduate Math Student Pushes Frontier of Graph Theory
At 21, Ashwin Sah has produced a body of work that senior mathematicians say is nearly unprecedented for a college student.
ICLR 2021 Graph Papers
Here is a list of graph papers with their final scores. This is in addition to the list for all the papers. Overall, 74 papers (out of 208 graph papers) increased their scores and 18 decreased.
Here is a list of graph papers with their final scores. This is in addition to the list for all the papers. Overall, 74 papers (out of 208 graph papers) increased their scores and 18 decreased.
Twitter
Sergey Ivanov
#ICLR2021 reviews are finalized and below is a final table with scores. Here is a teaser of how rebuttal helps the papers. ๐งต1/n https://t.co/GISD8ukXWs
Geometric Deep Learning: Successes, Challenges, Next Steps
A good video overview of the field by Michael Bronstein: talking about deriving convolution from the first principles, first GNN models, expressivity of GNNs, and the future applications of the field.
A good video overview of the field by Michael Bronstein: talking about deriving convolution from the first principles, first GNN models, expressivity of GNNs, and the future applications of the field.
YouTube
DVL Guest Lectures - Michael Bronstein, Imperial College London
Guest lecture by Michael Bronstein (Imperial College London, USI, Twitter)
Abstract: Geometric deep learning has recently become one of the hottest topics in machine learning, with its particular instance, graph neural networks, being used in a broad spectrumโฆ
Abstract: Geometric deep learning has recently become one of the hottest topics in machine learning, with its particular instance, graph neural networks, being used in a broad spectrumโฆ
Open Access Theses and Dissertations
Seeking for an inspiration for your dissertation or maybe want to check the latest monolithic works in graph community, take a look at OATD portal. Here is for example a search for all dissertations that have
Seeking for an inspiration for your dissertation or maybe want to check the latest monolithic works in graph community, take a look at OATD portal. Here is for example a search for all dissertations that have
graph in their title, resulting in ~400 PhD and ~100 MSc theses just in 2016-2020 period.Don't crack under pressure
I remember when I was interning at HKUST during my master's years I had a chance to see a motivational presentation for freshmen from one of the tenured professors there. One of the things he was emphasizing is that PhD is stressful experience with lots of uncertainty and you should keep being focused, brave and don't crack under the pressure. Here is a funny story that demonstrates it. A guy realized he has a bug 2 weeks before submitting his PhD thesis and asked MathOverflow community to help him to fix it, luckily it worked out well.
I remember when I was interning at HKUST during my master's years I had a chance to see a motivational presentation for freshmen from one of the tenured professors there. One of the things he was emphasizing is that PhD is stressful experience with lots of uncertainty and you should keep being focused, brave and don't crack under the pressure. Here is a funny story that demonstrates it. A guy realized he has a bug 2 weeks before submitting his PhD thesis and asked MathOverflow community to help him to fix it, luckily it worked out well.
MathOverflow
Issue UPDATE: in graph theory, different definitions of edge crossing numbers - impact on applications?
QUICK FINAL UPDATE: Just wanted to thank you MO users for all your support. Special thanks for the fast answers, I've accepted first one, appreciated the clarity it gave me. I've updated my torus
Graph Mining & Learning Workshop NeurIPS 2020
NeurIPS 2020 just started and there is a good workshop by Google Research on graph mining (by Bryan Perozzi and others). To see the videos you need to register for expo but it's free. Then you will have links to videos. There is also a slide deck with 312 pages about many interesting topics.
Update: it seems there is a bug in the registration panel, so you can access the schedule and videos in this page.
NeurIPS 2020 just started and there is a good workshop by Google Research on graph mining (by Bryan Perozzi and others). To see the videos you need to register for expo but it's free. Then you will have links to videos. There is also a slide deck with 312 pages about many interesting topics.
Update: it seems there is a bug in the registration panel, so you can access the schedule and videos in this page.
Graph Mining & Learning
Graph Mining and Learning @ NeurIPS
at the 2020 Neural Information Processing Systems Conference
Fresh picks from ArXiv
Today at ArXiv: approximate descent for training GNNs, expressive power of GNN, and GNN-doctor ๐
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNN
- Learning Graph Neural Networks with Approximate Gradient Descent AAAI 2021
- Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition ECCV 20
- Learning Node Representations from Noisy Graph Structures ICDM 2020
- Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs Workshop NeurIPS 2020
- Graph Neural Networks for Improved El Niรฑo Forecasting Workshop NeurIPS 2020
Applications
- Bayesian Graph Neural Networks for Molecular Property Prediction
- Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing
- Partial Gromov-Wasserstein Learning for Partial Graph Matching
- Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results with Stefanie Jegelka
- Graph Mixture Density Networks
Survey
- A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Today at ArXiv: approximate descent for training GNNs, expressive power of GNN, and GNN-doctor ๐
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNN
- Learning Graph Neural Networks with Approximate Gradient Descent AAAI 2021
- Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition ECCV 20
- Learning Node Representations from Noisy Graph Structures ICDM 2020
- Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs Workshop NeurIPS 2020
- Graph Neural Networks for Improved El Niรฑo Forecasting Workshop NeurIPS 2020
Applications
- Bayesian Graph Neural Networks for Molecular Property Prediction
- Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing
- Partial Gromov-Wasserstein Learning for Partial Graph Matching
- Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results with Stefanie Jegelka
- Graph Mixture Density Networks
Survey
- A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
MoleculeKit: Machine Learning Methods for Molecular Property Prediction and Drug Discovery
MoleculeKit is a new framework that deals with molecule predictions. It represents molecules as both graphs and sequences and then apply GNN or kernel together with BERT for downstream molecular tasks (predicting properties of nodes or graphs).
MoleculeKit is a new framework that deals with molecule predictions. It represents molecules as both graphs and sequences and then apply GNN or kernel together with BERT for downstream molecular tasks (predicting properties of nodes or graphs).
Privacy-Preserving Deep Learning Over Graphs
60 slides of overview of the emerging field of privacy-preserving GNNs. Could be interesting if you search for a new research topic.
60 slides of overview of the emerging field of privacy-preserving GNNs. Could be interesting if you search for a new research topic.
Graph Machine Learning research groups: Max Welling
I do a series of posts on the groups in graph research, previous post is here. The 20th is Max Welling, the head of the Amsterdam Machine Learning Lab. He co-founded a startup Scyfer BV that was acquired by Qualcomm, where he serves as VP of technologies. Max has a diverse research interests, including lately developments in graph machine learning field.
Max Welling (1968)
- Affiliation: University of Amsterdam, Qualcomm
- Education: Ph.D. at Utrecht University in 1998 (advisor: Gerard 't Hooft)
- h-index 73
- Awards: ECCV Koenderink Prize, ICML best papers.
- Interests: equivariant networks, variational encoders, GNNs.
I do a series of posts on the groups in graph research, previous post is here. The 20th is Max Welling, the head of the Amsterdam Machine Learning Lab. He co-founded a startup Scyfer BV that was acquired by Qualcomm, where he serves as VP of technologies. Max has a diverse research interests, including lately developments in graph machine learning field.
Max Welling (1968)
- Affiliation: University of Amsterdam, Qualcomm
- Education: Ph.D. at Utrecht University in 1998 (advisor: Gerard 't Hooft)
- h-index 73
- Awards: ECCV Koenderink Prize, ICML best papers.
- Interests: equivariant networks, variational encoders, GNNs.
Telegram
Graph Machine Learning
Graph Machine Learning research groups: Jimeng Sun
I do a series of posts on the groups in graph research, previous post is here. The 19th is Jimeng Sun, the head of SunLab at UIUC, teaching the courses of Big Data Analytics and Healthcare as well as Computingโฆ
I do a series of posts on the groups in graph research, previous post is here. The 19th is Jimeng Sun, the head of SunLab at UIUC, teaching the courses of Big Data Analytics and Healthcare as well as Computingโฆ
Deep Graph Networks Reading Group
There is a reading group at Bicocca University (Milan, Italy). Next session will happen on Monday, 14th December at 10am (UK time). The paper "HATS a hierarchical graph attention network for stock movement prediction" will be discussed. If you want to join you can get a link by contacting @Sagax_ita or via [email protected].
There is a reading group at Bicocca University (Milan, Italy). Next session will happen on Monday, 14th December at 10am (UK time). The paper "HATS a hierarchical graph attention network for stock movement prediction" will be discussed. If you want to join you can get a link by contacting @Sagax_ita or via [email protected].
Google
Dimitri Ognibene's Homepage - Graph Net - Reading Group
Deep Graph Networks Reading Group
Machine Learning on Knowledge Graphs @ NeurIPS 2020
A timely digest of NeurIPS 2020 by Michael Galkin. He speaks on improvement over Query2Box, how NAS and meta-learning works in KG domain, constructing the queries from the natural language, and several KG datasets. Worth a read!
A timely digest of NeurIPS 2020 by Michael Galkin. He speaks on improvement over Query2Box, how NAS and meta-learning works in KG domain, constructing the queries from the natural language, and several KG datasets. Worth a read!
Medium
Machine Learning on Knowledge Graphs @ NeurIPS 2020
Your guide to the KG-related research in NLP, December edition
GML Newsletter - Issue #5: Was 2020 a good year for graph research?
My new newsletter is out! ๐ฅ Talking about my predictions for 2020, NeurIPS recordings, ICLR submissions and a few links that you probably have seen already, my friends!
My new newsletter is out! ๐ฅ Talking about my predictions for 2020, NeurIPS recordings, ICLR submissions and a few links that you probably have seen already, my friends!
Fresh picks from ArXiv
Today at ArXiv: application of GNNs to drug discovery, graph construction by Wallmart, and improving expressiveness via more injective functions ๐
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNN
- Breaking the Expressive Bottlenecks of Graph Neural Networks
- Building Graphs at a Large Scale: Union Find Shuffle
- Utilising Graph Machine Learning within Drug Discovery and Development with Michael Bronstein
- Molecular graph generation with Graph Neural Networks
Conferences
- GDPNet: Refining Latent Multi-View Graph for Relation Extraction AAAI 2021
- Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation AAAI 2021
- Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation AAAI 2021
- Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation AAAI 2021
- Context-Aware Graph Convolution Network for Target Re-identification AAAI 2021
- Overcoming Catastrophic Forgetting in Graph Neural Networks AAAI 2021
- Bipartite Graph Embedding via Mutual Information Maximization WSDM 2021
- A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings Workshop NeurIPS 2021
- Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction Workshop NeurIPS 2020
Survey
- Deep Analysis on Subgraph Isomorphism
- The Future is Big Graphs! A Community View on Graph Processing Systems
- A Note on Spectral Graph Neural Network
Today at ArXiv: application of GNNs to drug discovery, graph construction by Wallmart, and improving expressiveness via more injective functions ๐
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNN
- Breaking the Expressive Bottlenecks of Graph Neural Networks
- Building Graphs at a Large Scale: Union Find Shuffle
- Utilising Graph Machine Learning within Drug Discovery and Development with Michael Bronstein
- Molecular graph generation with Graph Neural Networks
Conferences
- GDPNet: Refining Latent Multi-View Graph for Relation Extraction AAAI 2021
- Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation AAAI 2021
- Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation AAAI 2021
- Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation AAAI 2021
- Context-Aware Graph Convolution Network for Target Re-identification AAAI 2021
- Overcoming Catastrophic Forgetting in Graph Neural Networks AAAI 2021
- Bipartite Graph Embedding via Mutual Information Maximization WSDM 2021
- A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings Workshop NeurIPS 2021
- Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction Workshop NeurIPS 2020
Survey
- Deep Analysis on Subgraph Isomorphism
- The Future is Big Graphs! A Community View on Graph Processing Systems
- A Note on Spectral Graph Neural Network
How Knowledge Graphs Will Transform Data Management And Business
Nice article that describes how different companies including BenevolentAI are using knowledge graphs and what are the challenges of using them.
Nice article that describes how different companies including BenevolentAI are using knowledge graphs and what are the challenges of using them.
The Innovator
How Knowledge Graphs Will Transform Data Management And Business - The Innovator
Knowledge graphs are giving companies new insights into their businesses and helping them create new services and improve R&D research.
Generalization Bounds of GNN
Expressiveness, that is what class of graphs can be represented by GNN, has been extensively studied during the last two years. On the other hand, generalization, i.e. ability to represent correctly unseen graphs is just gaining attention. Here are some papers that study generalization of GNN.
- Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks NeurIPS 2020
- Generalization and Representational Limits of Graph Neural Networks ICML 2020
- Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case ICML 2020
- A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks Arxiv Dec 2020
Expressiveness, that is what class of graphs can be represented by GNN, has been extensively studied during the last two years. On the other hand, generalization, i.e. ability to represent correctly unseen graphs is just gaining attention. Here are some papers that study generalization of GNN.
- Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks NeurIPS 2020
- Generalization and Representational Limits of Graph Neural Networks ICML 2020
- Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case ICML 2020
- A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks Arxiv Dec 2020
Machine Learning for Graphs and Sequential Data (MLGS)
Awesome course by Stephan Gรผnnemann covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more โญ
Awesome course by Stephan Gรผnnemann covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more โญ