Graph Neural Networks through the lens of Differential Geometry and Algebraic Topology
And Michael is back with a first post in the series of posts on the connection between GML and differential geometry and algebraic topology. We've been waiting for this!
And Michael is back with a first post in the series of posts on the connection between GML and differential geometry and algebraic topology. We've been waiting for this!
Medium
Graph Neural Networks through the lens of Differential Geometry and Algebraic Topology
New perspectives on old problems in Graph ML
Fresh picks from ArXiv
This week on ArXiv: generalization guarantees, explaining bio recommendations, and learning over cosmos data 🌕
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNNs
* Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
* Federated Social Recommendation with Graph Neural Network with Philip S. Yu
* Pre-training Graph Neural Network for Cross Domain Recommendation with Philip S. Yu
* Inferring halo masses with Graph Neural Networks
* Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs
Benchmark
* GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation
Hardware
* QGTC: Accelerating Quantized GNN via GPU Tensor Core
This week on ArXiv: generalization guarantees, explaining bio recommendations, and learning over cosmos data 🌕
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNNs
* Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
* Federated Social Recommendation with Graph Neural Network with Philip S. Yu
* Pre-training Graph Neural Network for Cross Domain Recommendation with Philip S. Yu
* Inferring halo masses with Graph Neural Networks
* Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs
Benchmark
* GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation
Hardware
* QGTC: Accelerating Quantized GNN via GPU Tensor Core
Successful Phase I Cancer Vaccine Trials Powered by Graph ML
Transgene and NEC Corporation published a press release on the successful Phase I trials of TG4050, neoantigen cancer vaccine, tested on ovarian cancer, head and neck cancer. The release outlines the NEC's Neoantigen Prediction System based on Graph ML algorithms.
We reached out to Mathias Niepert, Chief Research Scientist of NEC Labs, to shed a bit more light on the graph ml setup and he kindly provided a few interesting details! Mathias says:
The main graph ML method is derived from Embedding Propagation which is a GNN that’s trained in an unsupervised way and, crucially, is able to handle / impute missing data in embedding space. The most relevant papers are Learning Graph Representations with Embedding Propagation (NeurIPS 2017) and Learning Representations of Missing Data for Predicting Patient Outcomes
A major challenge is that for each neoantigen we have some measurements but not all. Obtaining some of these requires expensive tests and some have to be collected from previous biomedical studies. One ends up with several very different feature types (requiring different ML encoders) and, for each such feature type, we only sometimes have a value. The graph-based ML method helps to impute and learn a unifying embedding space.
The graph itself is created based on specific similarity measures between proteins and is not given a-priori. Having a general graph, the task is to rank peptide candidates which would be most efficient for a given patient.
From a probe of a patient's cancer and healthy cells, you get several tens of thousands of neoantigen candidates. To manufacture a personalized vaccine, you have to narrow this down to several dozen candidates. These candidates should have two properties (1) likelihood to elicit immune response (2) different from healthy cell antigen. You end up scoring the neoantigens with the ML method, take the top K, and based on these you synthesize the vaccine. Graph ML is one component of a pretty complex system.
Mathias would like to emphasize that this is based on the work of several people at NEC and most credit should go to the domain experts who have collected the data and adapted and applied the graph ML methods to this problem.
Transgene and NEC Corporation published a press release on the successful Phase I trials of TG4050, neoantigen cancer vaccine, tested on ovarian cancer, head and neck cancer. The release outlines the NEC's Neoantigen Prediction System based on Graph ML algorithms.
We reached out to Mathias Niepert, Chief Research Scientist of NEC Labs, to shed a bit more light on the graph ml setup and he kindly provided a few interesting details! Mathias says:
The main graph ML method is derived from Embedding Propagation which is a GNN that’s trained in an unsupervised way and, crucially, is able to handle / impute missing data in embedding space. The most relevant papers are Learning Graph Representations with Embedding Propagation (NeurIPS 2017) and Learning Representations of Missing Data for Predicting Patient Outcomes
A major challenge is that for each neoantigen we have some measurements but not all. Obtaining some of these requires expensive tests and some have to be collected from previous biomedical studies. One ends up with several very different feature types (requiring different ML encoders) and, for each such feature type, we only sometimes have a value. The graph-based ML method helps to impute and learn a unifying embedding space.
The graph itself is created based on specific similarity measures between proteins and is not given a-priori. Having a general graph, the task is to rank peptide candidates which would be most efficient for a given patient.
From a probe of a patient's cancer and healthy cells, you get several tens of thousands of neoantigen candidates. To manufacture a personalized vaccine, you have to narrow this down to several dozen candidates. These candidates should have two properties (1) likelihood to elicit immune response (2) different from healthy cell antigen. You end up scoring the neoantigens with the ML method, take the top K, and based on these you synthesize the vaccine. Graph ML is one component of a pretty complex system.
Mathias would like to emphasize that this is based on the work of several people at NEC and most credit should go to the domain experts who have collected the data and adapted and applied the graph ML methods to this problem.
NEC
Transgene and NEC announce positive preliminary data from Phase I studies of TG4050, a novel individualized neoantigen cancer vaccine
Positive initial data generated in the first six patients treated with TG4050 demonstrate the strong potential of this individualized immunotherapy in ovarian cancer and head and neck cancer
Fresh picks from ArXiv
This week on ArXiv: feature propagation to alleviate missing node features, new sota for molecular prediction, and benchmarks on GNN explanations 👴
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNNs
* Multi-fidelity Stability for Graph Representation Learning with Joan Bruna
* AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
* Demystifying Graph Neural Network Explanations
* Unsupervised Learning for Identifying High Eigenvector Centrality Nodes: A Graph Neural Network Approach
* On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features with Michael Bronstein
* Directional Message Passing on Molecular Graphs via Synthetic Coordinates with Stephan Günnemann
This week on ArXiv: feature propagation to alleviate missing node features, new sota for molecular prediction, and benchmarks on GNN explanations 👴
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNNs
* Multi-fidelity Stability for Graph Representation Learning with Joan Bruna
* AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
* Demystifying Graph Neural Network Explanations
* Unsupervised Learning for Identifying High Eigenvector Centrality Nodes: A Graph Neural Network Approach
* On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features with Michael Bronstein
* Directional Message Passing on Molecular Graphs via Synthetic Coordinates with Stephan Günnemann
Over-squashing, Bottlenecks, and Graph Ricci curvature
A second post by Michael Bronstein about over-squashing effect, when exponentially many neighbors aggregate their information into a fix-sized vector, causing the loss of the information. In this post, Michael connects over-squashing effect with Ricci flow — a well-studied notion of the space curvature that is used in differential geometry.
A second post by Michael Bronstein about over-squashing effect, when exponentially many neighbors aggregate their information into a fix-sized vector, causing the loss of the information. In this post, Michael connects over-squashing effect with Ricci flow — a well-studied notion of the space curvature that is used in differential geometry.
Medium
Over-squashing, Bottlenecks, and Graph Ricci curvature
A concept from differential geometry called Ricci curvature allows to understand the phenomena of over-squashing and bottlenecks in GNNs
Connected Data World 2021
Today at 14-00 (Paris time), I will be in the panel at Connected Data World conference, with a great line of graph ML researchers to talk about applications of GNNs. Besides there are many other interesting talks talking about ML, knowledge graphs, graph databases, and more. If you want to attend, you need to register (there is a free streaming track and also a discount code CDW21SPEAKERA20 for a full program).
Today at 14-00 (Paris time), I will be in the panel at Connected Data World conference, with a great line of graph ML researchers to talk about applications of GNNs. Besides there are many other interesting talks talking about ML, knowledge graphs, graph databases, and more. If you want to attend, you need to register (there is a free streaming track and also a discount code CDW21SPEAKERA20 for a full program).
Connected Data World Conference | December 1-2-3rd 2021
Connected Data World 2021. Connecting Data, People, & Ideas since 2016.
Mathematical discoveries take intuition and creativity – and now a little help from AI
A new work published in Nature with Petar Veličković and his colleagues shows how GNNs can guide the intuition of mathematicians in both representation theory & knot theory. In a more mathematical manunoscript they prove a combinatorial conjecture from 80s with the help of GNNs. This article also describes their discovery.
A new work published in Nature with Petar Veličković and his colleagues shows how GNNs can guide the intuition of mathematicians in both representation theory & knot theory. In a more mathematical manunoscript they prove a combinatorial conjecture from 80s with the help of GNNs. This article also describes their discovery.
Nature
Advancing mathematics by guiding human intuition with AI
Nature - A framework through which machine learning can guide mathematicians in discovering new conjectures and theorems is presented and shown to yield mathematical insight on important open...
Tutorial: Message Passing In Machine Learning
At NeurIPS 2021, there is a tutorial Message Passing In Machine Learning by Wee Sun Lee. It discusses in depth probabilistic graphical models (belief propagation, variational inference, mean field), markov decision processes (value iteration networks), GNNs and attention networks. It will be live today for registered people. Slides are available online.
At NeurIPS 2021, there is a tutorial Message Passing In Machine Learning by Wee Sun Lee. It discusses in depth probabilistic graphical models (belief propagation, variational inference, mean field), markov decision processes (value iteration networks), GNNs and attention networks. It will be live today for registered people. Slides are available online.
neurips.cc
NeurIPS 2021 Schedule
NeurIPS Website
Fresh picks from ArXiv
This week on ArXiv: library for graph recommendations, embeddings for molecules, and self-explaining GNNs 🦜
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNNs
* Fast Graph Neural Tangent Kernel via Kronecker Sketching AAAI 22
* ProtGNN: Towards Self-Explaining Graph Neural Networks AAAI 22
* Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems
* Imbalanced Graph Classification via Graph-of-Graph Neural Networks
* Molecular Contrastive Learning with Chemical Element Knowledge Graph AAAI 22
* Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks
This week on ArXiv: library for graph recommendations, embeddings for molecules, and self-explaining GNNs 🦜
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNNs
* Fast Graph Neural Tangent Kernel via Kronecker Sketching AAAI 22
* ProtGNN: Towards Self-Explaining Graph Neural Networks AAAI 22
* Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems
* Imbalanced Graph Classification via Graph-of-Graph Neural Networks
* Molecular Contrastive Learning with Chemical Element Knowledge Graph AAAI 22
* Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks
Open PhD/PostDoc Positions on GML
Aleksandar Bojchevski, whom you may know by his works on robustness, adversarial attacks, generative models on graphs, hires PhDs and PostDocs to work on trustworthy machine learning research. The position is at CISPA Helmholtz Center for Information Security, in Saarland, Germany and is well paid (for PhDs around 4K euros per month before taxes). Apply here.
Aleksandar Bojchevski, whom you may know by his works on robustness, adversarial attacks, generative models on graphs, hires PhDs and PostDocs to work on trustworthy machine learning research. The position is at CISPA Helmholtz Center for Information Security, in Saarland, Germany and is well paid (for PhDs around 4K euros per month before taxes). Apply here.
abojchevski.github.io
Aleksandar Bojchevski
Full Professor @ University of Cologne
Fresh picks from ArXiv
This week on ArXiv: belief propagation guarantees, scaling molecular GNNs, and debunking multi-label graph embeddings 🧙
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNN
* A Comparative Study on Robust Graph Neural Networks to Structural Noises
* Convergence of Generalized Belief Propagation Algorithm on Graphs with Motifs
* LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks AAAI 2022
* A Self-supervised Mixed-curvature Graph Neural Network AAAI 2022
* Robustification of Online Graph Exploration Methods AAAI 2022
* Neural Belief Propagation for Scene Graph Generation
* Adaptive Kernel Graph Neural Network AAAI 2022
* On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations AAAI 2022
* Scalable Geometric Deep Learning on Molecular Graphs
* OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
This week on ArXiv: belief propagation guarantees, scaling molecular GNNs, and debunking multi-label graph embeddings 🧙
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNN
* A Comparative Study on Robust Graph Neural Networks to Structural Noises
* Convergence of Generalized Belief Propagation Algorithm on Graphs with Motifs
* LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks AAAI 2022
* A Self-supervised Mixed-curvature Graph Neural Network AAAI 2022
* Robustification of Online Graph Exploration Methods AAAI 2022
* Neural Belief Propagation for Scene Graph Generation
* Adaptive Kernel Graph Neural Network AAAI 2022
* On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations AAAI 2022
* Scalable Geometric Deep Learning on Molecular Graphs
* OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
PhD positions at the University of Vienna
Nils M. Kriege, whom you may know from his works on graph kernels, TUDatasets, Weisfeiler-Leman embeddings and other important line of research in GML, now has two PhD positions at the University of Vienna. You can read more about these positions here and here. Application deadline December 20th. You can apply here.
Nils M. Kriege, whom you may know from his works on graph kernels, TUDatasets, Weisfeiler-Leman embeddings and other important line of research in GML, now has two PhD positions at the University of Vienna. You can read more about these positions here and here. Application deadline December 20th. You can apply here.
New Deep Learning Model Could Accelerate the Process of Discovering New Medicines
An article on recent GeoMol model that generates new molecules in an end-to-end, non-autoregressive and SE(3)-invariant fashion.
An article on recent GeoMol model that generates new molecules in an end-to-end, non-autoregressive and SE(3)-invariant fashion.
SciTechDaily
New Deep Learning Model Could Accelerate the Process of Discovering New Medicines
Taking Some of the Guesswork Out of Drug Discovery A deep learning model rapidly predicts the 3D shapes of drug-like molecules, which could accelerate the process of discovering new medicines. In their quest to discover effective new medicines, scientists…
Machine Learning and Simulation Science (MLSim) Group Positions
Mathias Niepert, whom you may know by the recent breakthrough that applies GML for cancer drug discovery, now hires PhDs and Postdocs at the University of Stuttgart. The application deadline is January 15th 2022. Find out more here.
Mathias Niepert, whom you may know by the recent breakthrough that applies GML for cancer drug discovery, now hires PhDs and Postdocs at the University of Stuttgart. The application deadline is January 15th 2022. Find out more here.
Telegram
Graph Machine Learning
Successful Phase I Cancer Vaccine Trials Powered by Graph ML
Transgene and NEC Corporation published a press release on the successful Phase I trials of TG4050, neoantigen cancer vaccine, tested on ovarian cancer, head and neck cancer. The release outlines…
Transgene and NEC Corporation published a press release on the successful Phase I trials of TG4050, neoantigen cancer vaccine, tested on ovarian cancer, head and neck cancer. The release outlines…
ICLR 2022 Workshops Announcement
The list of accepted workshops at ICLR 2022 has just been announced! There is a good bunch of GraphML-related workshops you can send your paper to:
- Geometrical and Topological Representation Learning
- Deep Learning on Graphs for Natural Language Processing
- Machine Learning for Drug Discovery (MLDD)
- Deep Generative Models for Highly Structured Data
- Workshop on the Elements of Reasoning: Objects, Structure and Causality
Workshop websites and calls are coming soon, stay tuned.
The list of accepted workshops at ICLR 2022 has just been announced! There is a good bunch of GraphML-related workshops you can send your paper to:
- Geometrical and Topological Representation Learning
- Deep Learning on Graphs for Natural Language Processing
- Machine Learning for Drug Discovery (MLDD)
- Deep Generative Models for Highly Structured Data
- Workshop on the Elements of Reasoning: Objects, Structure and Causality
Workshop websites and calls are coming soon, stay tuned.
Graph ML in 2022
The editors of the Graph ML channel proudly present the winter longread (in collaboration with Anton Tsitsulin and Anvar Kurmukov) including major research trends in 2021:
- Graph Transformers
- Equivariant GNNs
- Generative Models for Molecules
- GNNs and Combinatorial Optimization
- Subgraph GNNs
- Scalable and Deep GNNs
- Knowledge Graph Representation Learning
- Generally Cool Research with GNNs
Besides that, the post describes new datasets and challenges, new courses and books, as well as new / updated open source libraries for graph representation learning.
The editors of the Graph ML channel proudly present the winter longread (in collaboration with Anton Tsitsulin and Anvar Kurmukov) including major research trends in 2021:
- Graph Transformers
- Equivariant GNNs
- Generative Models for Molecules
- GNNs and Combinatorial Optimization
- Subgraph GNNs
- Scalable and Deep GNNs
- Knowledge Graph Representation Learning
- Generally Cool Research with GNNs
Besides that, the post describes new datasets and challenges, new courses and books, as well as new / updated open source libraries for graph representation learning.
Medium
Graph ML in 2022: Where Are We Now?
Hot trends and major advancements
GNN User Group Videos 2021
NVIDIA and AWS DGL teams wish you a wonderful Holiday Season and a Happy New Year and to stay connect with their 1,000+ members you can follow their slack channel. You may also watch the replays from our 25 global speakers who made the meetups possible in 2021.
• 12/9/2021 Session: Neptune ML: Graph Machine Learning meets Graph Database (Dr. Xiang Song, AWS AI Research and Education Lab & Joy Wang, Amazon Neptune) and Atomistic Line Graph Neural Network for improved materials property predictions (Dr. Kamal Choudhary, National Institute of Standards and Technology (NIST), Maryland).
• 10/28/2021 Session: Large-scale GNN training with DGL (Da Zheng Ph.D., Amazon) and New Trends and Results in Graph Federated Learning (Prof. Carl Yang, Emory University).
• 9/30/2021 Session: Unified Tensor - Enabling GPU-centric Data Access for Efficient Large Graph GNN Training (Seungwon Min, University of Illinois at Urbana-Champaign) and Challenges and Thinking in Go-production of GNN + DGL (Dr. Jian Zhang, AWS Shanghai AI Lab and AWS Machine Learning Solution Lab).
• 7/29/2021 Session: DGL 0.7 release (Dr. Minjie Wang, Amazon), Storing Node Features in GPU memory to speedup billion-scale GNN training (Dr. Dominique LaSalle, NVIDIA), Locally Private Graph Neural Networks (Sina Sajadmanesh, Idiap Research Institute, Switzerland) and Graph Embedding and Application in Meituan (Mengdi Zhang, Meituan).
• 6/24/2021 Session: Binary Graph Neural Networks and Dynamic Graph Models (Mehdi Bahri, Imperial College London) and Simplifying large-scale visual analysis of tricky data & models with GPUs, graphs, and ML (Leo Meyerovich, Graphistry Inc).
• 5/27/2021 Session: Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration (Mahdi Saleh, TUM) Optimizing Graph Transformer Networks with Graph-based Techniques (Loc Hoang, University of Texas at Austin) and Encoding the Core Business Entities Using Meituan Brain (Mengdi Zhang, Meituan).
• 4/29/2021 Session: Boost then Convolve: Gradient Boosting Meets Graph Neural Networks (Dr. Sergey Ivanov, Criteo, Russia) and Inductive Representation Learning of Temporal Networks via Causal Anonymous Walks (Prof. Pan Li, Purdue University).
• 3/25/2021 Session: Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics (Prof. Marinka Zitnik & Kexin Huang, Harvard University) and The Transformer Network for the Traveling Salesman Problem (Prof. Xavier Bresson, Nanyang Technological University (NTU), Singapore).
• 2/25/2021 Session: Gunrock: Graph Analytics on GPU (Dr. John Owens, University of California, Davis), NVIDIA CuGraph - An Open-Source Package for Graphs (Dr. Joe Eaton, NVIDIA) and Exploitation on Learning Mechanism of GNN (Dr. Chuan Shi, Beijing University of Posts and Telecommunications).
• 1/28/2021 Session: A Framework For Differentiable Discovery of Graph Algorithms (Dr. Le Song, Georgia Tech).
NVIDIA and AWS DGL teams wish you a wonderful Holiday Season and a Happy New Year and to stay connect with their 1,000+ members you can follow their slack channel. You may also watch the replays from our 25 global speakers who made the meetups possible in 2021.
• 12/9/2021 Session: Neptune ML: Graph Machine Learning meets Graph Database (Dr. Xiang Song, AWS AI Research and Education Lab & Joy Wang, Amazon Neptune) and Atomistic Line Graph Neural Network for improved materials property predictions (Dr. Kamal Choudhary, National Institute of Standards and Technology (NIST), Maryland).
• 10/28/2021 Session: Large-scale GNN training with DGL (Da Zheng Ph.D., Amazon) and New Trends and Results in Graph Federated Learning (Prof. Carl Yang, Emory University).
• 9/30/2021 Session: Unified Tensor - Enabling GPU-centric Data Access for Efficient Large Graph GNN Training (Seungwon Min, University of Illinois at Urbana-Champaign) and Challenges and Thinking in Go-production of GNN + DGL (Dr. Jian Zhang, AWS Shanghai AI Lab and AWS Machine Learning Solution Lab).
• 7/29/2021 Session: DGL 0.7 release (Dr. Minjie Wang, Amazon), Storing Node Features in GPU memory to speedup billion-scale GNN training (Dr. Dominique LaSalle, NVIDIA), Locally Private Graph Neural Networks (Sina Sajadmanesh, Idiap Research Institute, Switzerland) and Graph Embedding and Application in Meituan (Mengdi Zhang, Meituan).
• 6/24/2021 Session: Binary Graph Neural Networks and Dynamic Graph Models (Mehdi Bahri, Imperial College London) and Simplifying large-scale visual analysis of tricky data & models with GPUs, graphs, and ML (Leo Meyerovich, Graphistry Inc).
• 5/27/2021 Session: Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration (Mahdi Saleh, TUM) Optimizing Graph Transformer Networks with Graph-based Techniques (Loc Hoang, University of Texas at Austin) and Encoding the Core Business Entities Using Meituan Brain (Mengdi Zhang, Meituan).
• 4/29/2021 Session: Boost then Convolve: Gradient Boosting Meets Graph Neural Networks (Dr. Sergey Ivanov, Criteo, Russia) and Inductive Representation Learning of Temporal Networks via Causal Anonymous Walks (Prof. Pan Li, Purdue University).
• 3/25/2021 Session: Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics (Prof. Marinka Zitnik & Kexin Huang, Harvard University) and The Transformer Network for the Traveling Salesman Problem (Prof. Xavier Bresson, Nanyang Technological University (NTU), Singapore).
• 2/25/2021 Session: Gunrock: Graph Analytics on GPU (Dr. John Owens, University of California, Davis), NVIDIA CuGraph - An Open-Source Package for Graphs (Dr. Joe Eaton, NVIDIA) and Exploitation on Learning Mechanism of GNN (Dr. Chuan Shi, Beijing University of Posts and Telecommunications).
• 1/28/2021 Session: A Framework For Differentiable Discovery of Graph Algorithms (Dr. Le Song, Georgia Tech).
YouTube
Graph Neural Networks User Group Meetup Thursday 12/9/2021
Agenda 12/9/2021:
4:00 - 4:05 PM (PST): Welcome and Updates.
4:05 - 4:35PM (PST): Neptune ML: Graph Machine Learning meets Graph Database (Dr. Xiang Song, AWS AI Research and Education Lab & Joy Wang, Amazon Neptune).
4:35 - 5:05 PM (PST): Atomistic Line…
4:00 - 4:05 PM (PST): Welcome and Updates.
4:05 - 4:35PM (PST): Neptune ML: Graph Machine Learning meets Graph Database (Dr. Xiang Song, AWS AI Research and Education Lab & Joy Wang, Amazon Neptune).
4:35 - 5:05 PM (PST): Atomistic Line…
What does 2022 hold for Geometric & Graph ML?
Michael Bronstein and Petar Veličković released a huge post summarizing the state of Graph ML in 2021 and predicting possible breakthroughs in 2022. Even more, the authors conducted a large-scale community study and interviewed many prominent researchers discussing 11 key aspects:
- Rising importance of geometry in ML
- Message passing GNNs are still dominating
- Differential equations power new GNN architectures
- Old ideas from signal processing, neuroscience, and physics strike back
- Modeling complex systems with higher-order structures
- Reasoning, axiomatisation, and generalisation are still challenging
- Graphs in Reinforcement Learning
- AlphaFold 2 is a paradigm shift in structural biology
- Progress in graph transformer architectures
- Drug discovery with Geometric and Graph ML
- Quantum ML + graph-based methods
And 130 references 😉
Michael Bronstein and Petar Veličković released a huge post summarizing the state of Graph ML in 2021 and predicting possible breakthroughs in 2022. Even more, the authors conducted a large-scale community study and interviewed many prominent researchers discussing 11 key aspects:
- Rising importance of geometry in ML
- Message passing GNNs are still dominating
- Differential equations power new GNN architectures
- Old ideas from signal processing, neuroscience, and physics strike back
- Modeling complex systems with higher-order structures
- Reasoning, axiomatisation, and generalisation are still challenging
- Graphs in Reinforcement Learning
- AlphaFold 2 is a paradigm shift in structural biology
- Progress in graph transformer architectures
- Drug discovery with Geometric and Graph ML
- Quantum ML + graph-based methods
And 130 references 😉
Medium
What does 2022 hold for Geometric & Graph ML?
Leading researchers in Geometric & Graph ML summarise the progress in 2021 and make predictions for 2022
Postdoc position at Univerity Milano-Bicocca with Dimitri Ognibene
University Milano-Bicocca is offering 1 postdoctoral position on machine learning and AI applied to understand and contrast social media threats for teenagers. The successful participant will be involved in the multidisciplinary project COURAGE funded by Volkswagen Foundation.
The position is research only starting in April 2022
Application Closes: T.B.D. (about mid-February)
Starts: April - May 2022
Duration: 1.5 years
Salary: € 3100 pm after tax
Please contact us for expression of interest and preliminary information:
dimitri.ognibene@unimib.it (PI)
f.lomonaco5@campus.unimib.it
Topics: Social media, NLP, machine learning, cognitive models, opinion dynamics, computer vision, reinforcement learning, graph neural network, recommender systems, network analysis
Project: COURAGE
Informal Denoscription: https://sites.google.com/site/dimitriognibenehomepage/jobs
University Milano-Bicocca is offering 1 postdoctoral position on machine learning and AI applied to understand and contrast social media threats for teenagers. The successful participant will be involved in the multidisciplinary project COURAGE funded by Volkswagen Foundation.
The position is research only starting in April 2022
Application Closes: T.B.D. (about mid-February)
Starts: April - May 2022
Duration: 1.5 years
Salary: € 3100 pm after tax
Please contact us for expression of interest and preliminary information:
dimitri.ognibene@unimib.it (PI)
f.lomonaco5@campus.unimib.it
Topics: Social media, NLP, machine learning, cognitive models, opinion dynamics, computer vision, reinforcement learning, graph neural network, recommender systems, network analysis
Project: COURAGE
Informal Denoscription: https://sites.google.com/site/dimitriognibenehomepage/jobs
Google
Dimitri Ognibene's Homepage - Jobs
Currently Open Positions
Job openings - ML for molecule design at Roche, Zurich
The Swiss ML group within Prescient Design/Roche is looking for (graph) ML and (computational/structural) biology (BIO) researchers. Apply if you want to use your neural network skills to design macromolecules & help improve lives.
More information: bit.ly/3HbqQ0w (ML scientist), bit.ly/3BG0ra2 (computational biologist)
Team: www.prescient.design, a member of the Roche group
Location: Zurich/Switzerland
Relevant work: bit.ly/36mimae
Timing: asap
Contact: andreas.loukas[at]roche.com
Keywords: machine learning, graph neural networks, geometric deep learning, generative models, invariant/equivariant neural nets, reinforcement learning, protein design, rosetta
The Swiss ML group within Prescient Design/Roche is looking for (graph) ML and (computational/structural) biology (BIO) researchers. Apply if you want to use your neural network skills to design macromolecules & help improve lives.
More information: bit.ly/3HbqQ0w (ML scientist), bit.ly/3BG0ra2 (computational biologist)
Team: www.prescient.design, a member of the Roche group
Location: Zurich/Switzerland
Relevant work: bit.ly/36mimae
Timing: asap
Contact: andreas.loukas[at]roche.com
Keywords: machine learning, graph neural networks, geometric deep learning, generative models, invariant/equivariant neural nets, reinforcement learning, protein design, rosetta
New Release of DGL v0.8
One of the most prominent libraries for graph machine learning, DGL, has recently released an updated v0.8 with new features as well as improvement on system performance. The highlights are:
- A major update of the mini-batch sampling pipeline.
- Significant acceleration and code simplification of heterogeneous GNNs and link-prediction models.
- GNNLens: a DGL empowered tool for GNN explanability.
- New functions to create, transform and augment graph datasets, e.g. in graph contrastive learning or repurposing a graph for different tasks.
- DGL-Go: a new GNN model training command line tool for quick experiments with SOTA models.
https://www.dgl.ai/release/2022/03/01/release.html
One of the most prominent libraries for graph machine learning, DGL, has recently released an updated v0.8 with new features as well as improvement on system performance. The highlights are:
- A major update of the mini-batch sampling pipeline.
- Significant acceleration and code simplification of heterogeneous GNNs and link-prediction models.
- GNNLens: a DGL empowered tool for GNN explanability.
- New functions to create, transform and augment graph datasets, e.g. in graph contrastive learning or repurposing a graph for different tasks.
- DGL-Go: a new GNN model training command line tool for quick experiments with SOTA models.
https://www.dgl.ai/release/2022/03/01/release.html
www.dgl.ai
Deep Graph Library
Library for deep learning on graphs