Knowledge Graphs @ ICLR 2021
One and only Michael Galkin does it again with a superior digest of knowledge graph research at ICLR 2021. Topics include reasoning, temporal logics, and complex question answering in KGs: a lot of novel ideas and less SOTA-chasing work!
One and only Michael Galkin does it again with a superior digest of knowledge graph research at ICLR 2021. Topics include reasoning, temporal logics, and complex question answering in KGs: a lot of novel ideas and less SOTA-chasing work!
Medium
Knowledge Graphs @ ICLR 2021
Your guide to the KG-related research in ML, May edition
Fresh picks from ArXiv
This week on ArXiv: optimization properties of GNNs, review on sample-based approaches, and time zigzags for Ethereum price prediction ๐ฐ
If I forgot to mention your paper, please shoot me a message and I will update the post.
Conferences
* Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders ACL 2021
* Neural Graph Matching based Collaborative Filtering SIGIR 2021
* Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting ICML 2021
* Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth ICML 2021
Efficiency
* Scalable Graph Neural Network Training: The Case for Sampling
* VersaGNN: a Versatile accelerator for Graph neural networks
This week on ArXiv: optimization properties of GNNs, review on sample-based approaches, and time zigzags for Ethereum price prediction ๐ฐ
If I forgot to mention your paper, please shoot me a message and I will update the post.
Conferences
* Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders ACL 2021
* Neural Graph Matching based Collaborative Filtering SIGIR 2021
* Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting ICML 2021
* Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth ICML 2021
Efficiency
* Scalable Graph Neural Network Training: The Case for Sampling
* VersaGNN: a Versatile accelerator for Graph neural networks
New Proof Reveals That Graphs With No Pentagons Are Fundamentally Different
A new article at Quanta about ErdลsโHajnal conjecture, which states that any graph that forbids having some small subgraph will inevitably have a large clique or a large independent set. The article talks about a recent paper that confirms the conjecture for a special case which was deemed the hardest. Now there is a hope that the conjecture is true for the general case.
A new article at Quanta about ErdลsโHajnal conjecture, which states that any graph that forbids having some small subgraph will inevitably have a large clique or a large independent set. The article talks about a recent paper that confirms the conjecture for a special case which was deemed the hardest. Now there is a hope that the conjecture is true for the general case.
Quanta Magazine
New Proof Reveals That Graphs With No Pentagons Are Fundamentally Different
Researchers have proved a special case of the Erdลs-Hajnal conjecture, which shows what happens in graphs that exclude anything resembling a pentagon.
PhD Thesis on Graph Machine Learning
Here are some PhD dissertations on GML. Part 4 (previous here).
Adji Bousso Dieng: Deep Probabilistic Graphical Modeling (Columbia University 2020)
Dai Quoc Nguyen: Representation Learning for Graph-Structured Data (Monash University 2021)
Matteo Tiezzi: Local Propagation in Neural Network Learning by Architectural Constraints (Universitร degli Studi di Siena 2021)
Here are some PhD dissertations on GML. Part 4 (previous here).
Adji Bousso Dieng: Deep Probabilistic Graphical Modeling (Columbia University 2020)
Dai Quoc Nguyen: Representation Learning for Graph-Structured Data (Monash University 2021)
Matteo Tiezzi: Local Propagation in Neural Network Learning by Architectural Constraints (Universitร degli Studi di Siena 2021)
Telegram
Graph Machine Learning
PhD Thesis on Graph Machine Learning
Here are some PhD dissertations on GML. Part 3 (previous here).
Xiaowen Dong: Multi-view signal processing and learning on graphs (EPFL 2014)
Yan Leng: Collective behavior over social networks with data-driven andโฆ
Here are some PhD dissertations on GML. Part 3 (previous here).
Xiaowen Dong: Multi-view signal processing and learning on graphs (EPFL 2014)
Yan Leng: Collective behavior over social networks with data-driven andโฆ
Constructions in combinatorics via neural networks
I have been fascinated about potential of using machine learning for combinatorial problems and have written multiple posts (here and here) and a survey about this. And as such it was exciting to see a work that applies RL framework to disprove several combinatorial conjectures.
The algorithm is very simple: generate many graphs with MLP, select the top-X of them, use cross-entropy to update MLP. So it does not use recent advances in RL, neither in GML to care about invariance of the input. So there is a room for improvement. Also it generates graphs of pre-determined size, so if a counterexample has a big order it would be difficult to know in advance. But it would be very interesting to apply this framework to more complicated conjectures such as reconstruction conjecture.
I have been fascinated about potential of using machine learning for combinatorial problems and have written multiple posts (here and here) and a survey about this. And as such it was exciting to see a work that applies RL framework to disprove several combinatorial conjectures.
The algorithm is very simple: generate many graphs with MLP, select the top-X of them, use cross-entropy to update MLP. So it does not use recent advances in RL, neither in GML to care about invariance of the input. So there is a room for improvement. Also it generates graphs of pre-determined size, so if a counterexample has a big order it would be difficult to know in advance. But it would be very interesting to apply this framework to more complicated conjectures such as reconstruction conjecture.
Fresh picks from ArXiv
This week on ArXiv: power of WL, explaining molecular GNNs, and a survey on NFTs ๐ผ
If I forgot to mention your paper, please shoot me a message and I will update the post.
Applications
* Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction SIGIR 2021
* Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction IJCAI 2021
* REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition
Algorithms
* Two Influence Maximization Games on Graphs Made Temporal IJCAI 2021
* Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding IJCAI 2021
GNNs
* Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning IJCAI 2021
* Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity ICML 2021
Survey
* Self-supervised on Graphs: Contrastive, Generative,or Predictive
* The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs with Christopher Morris, Matthias Fey, Nils M. Kriege, IJCAI 2021
* Graph Learning based Recommender Systems: A Review
* Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges
This week on ArXiv: power of WL, explaining molecular GNNs, and a survey on NFTs ๐ผ
If I forgot to mention your paper, please shoot me a message and I will update the post.
Applications
* Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction SIGIR 2021
* Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction IJCAI 2021
* REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition
Algorithms
* Two Influence Maximization Games on Graphs Made Temporal IJCAI 2021
* Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding IJCAI 2021
GNNs
* Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning IJCAI 2021
* Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity ICML 2021
Survey
* Self-supervised on Graphs: Contrastive, Generative,or Predictive
* The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs with Christopher Morris, Matthias Fey, Nils M. Kriege, IJCAI 2021
* Graph Learning based Recommender Systems: A Review
* Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges
On Explainability of Graph Neural Networks via Subgraph Explorations
This is a guest post by Shuiwang Ji about their recent work, accepted to ICML 2021.
Title: "On Explainability of Graph Neural Networks via Subgraph Explorations"
TL; DR:
- We propose a novel method, known as SubgraphX, to explain GNNs by exploring and identifying important subgraphs.
- We propose to incorporate the Monte Carlo tree search to explore subgraphs and propose efficient approximation schemes to measure subgraphs via Shapley values.
- Our proposed method consistently and significantly outperforms state-of-the-art techniques.
Code is now available as part of our DIG library.
We study the explainability of Graph Neural Networks and propose a novel method (SubgraphX) to provide subgraph-level explanations. While existing methods mainly focus on explaining GNNs with graph nodes or edges, we argue that subgraphs are more intuitive and human-intelligible.
In our SubgraphX, we propose to explore different subgraphs with the Monte Carlo tree search. For each subgraph, we measure its importance using Shapley values, which can capture the interactions among different graph structures. We further improve the efficiency with our proposed approximation schemes to compute Shapley values for graph data. Both quantitative and qualitative studies show our method obtain higher-quality and more human-intelligible explanations while keeping time complexity acceptable.
Our method represents the first attempt to explain GNNs by explicitly studying the subgraphs. We hope that this work can provide a new direction for the community to investigate the explainability of GNNs in the future.
This is a guest post by Shuiwang Ji about their recent work, accepted to ICML 2021.
Title: "On Explainability of Graph Neural Networks via Subgraph Explorations"
TL; DR:
- We propose a novel method, known as SubgraphX, to explain GNNs by exploring and identifying important subgraphs.
- We propose to incorporate the Monte Carlo tree search to explore subgraphs and propose efficient approximation schemes to measure subgraphs via Shapley values.
- Our proposed method consistently and significantly outperforms state-of-the-art techniques.
Code is now available as part of our DIG library.
We study the explainability of Graph Neural Networks and propose a novel method (SubgraphX) to provide subgraph-level explanations. While existing methods mainly focus on explaining GNNs with graph nodes or edges, we argue that subgraphs are more intuitive and human-intelligible.
In our SubgraphX, we propose to explore different subgraphs with the Monte Carlo tree search. For each subgraph, we measure its importance using Shapley values, which can capture the interactions among different graph structures. We further improve the efficiency with our proposed approximation schemes to compute Shapley values for graph data. Both quantitative and qualitative studies show our method obtain higher-quality and more human-intelligible explanations while keeping time complexity acceptable.
Our method represents the first attempt to explain GNNs by explicitly studying the subgraphs. We hope that this work can provide a new direction for the community to investigate the explainability of GNNs in the future.
GitHub
GitHub - divelab/DIG: A library for graph deep learning research
A library for graph deep learning research. Contribute to divelab/DIG development by creating an account on GitHub.
GraphDF: A Discrete Flow Model for Molecular Graph Generation
This is a guest post by Shuiwang Ji about their recent work, accepted to ICML 2021.
Title: โGraphDF: A Discrete Flow Model for Molecular Graph Generationโ
TL; DR:
- We propose GraphDF, a novel discrete latent variable model for molecular graph generation method.
- We propose to use invertible modulo shift transform to sequentially generate graph nodes and edges from discrete latent variables.
- Our proposed method outperforms prior methods on random generation, property optimization, and constrained optimization tasks.
Code is now available as part of our DIG library.
We study the molecular generation problem and propose a novel method (GraphDF) achieving new state-of-the-art performance. While prior methods use continuous latent variables, we argue that discrete latent variables are more suitable to model the categorical distribution of graph nodes and edges.
In our GraphDF, the molecular graph is generated by sequentially using modulo shift transform to convert a sampled discrete latent variable to the categorical number of the graph node or edge type. The use of discrete latent variables eliminates the bad effect of dequantization and models the underlying distribution of graph structures more accurately. The modulo shift transform captures conditional information from the last sub-graph by graph convolutional networks to ensure the order invariance. Comprehensive studies show that our method outperform prior methods on random generation, property optimization, and constrained optimization tasks.
Our method is the first work to model the density of complicated molecular graph data with discrete latent variables. We hope that it can provide a new insight for the community to explore more powerful graph generation models in the future.
This is a guest post by Shuiwang Ji about their recent work, accepted to ICML 2021.
Title: โGraphDF: A Discrete Flow Model for Molecular Graph Generationโ
TL; DR:
- We propose GraphDF, a novel discrete latent variable model for molecular graph generation method.
- We propose to use invertible modulo shift transform to sequentially generate graph nodes and edges from discrete latent variables.
- Our proposed method outperforms prior methods on random generation, property optimization, and constrained optimization tasks.
Code is now available as part of our DIG library.
We study the molecular generation problem and propose a novel method (GraphDF) achieving new state-of-the-art performance. While prior methods use continuous latent variables, we argue that discrete latent variables are more suitable to model the categorical distribution of graph nodes and edges.
In our GraphDF, the molecular graph is generated by sequentially using modulo shift transform to convert a sampled discrete latent variable to the categorical number of the graph node or edge type. The use of discrete latent variables eliminates the bad effect of dequantization and models the underlying distribution of graph structures more accurately. The modulo shift transform captures conditional information from the last sub-graph by graph convolutional networks to ensure the order invariance. Comprehensive studies show that our method outperform prior methods on random generation, property optimization, and constrained optimization tasks.
Our method is the first work to model the density of complicated molecular graph data with discrete latent variables. We hope that it can provide a new insight for the community to explore more powerful graph generation models in the future.
GitHub
GitHub - divelab/DIG: A library for graph deep learning research
A library for graph deep learning research. Contribute to divelab/DIG development by creating an account on GitHub.
Rethinking Graph Neural Architecture Search from Message-passing
With abundance of GNNs architectures it's natural to ask how to select the right architecture for your task. In a recent CVPR 2021 work propose a generic architecture that encompasses many existing GNNs, which is then optimized via gradient descent. After optimization resulted GNNs may get different architectures for each layer of GNNs.
With abundance of GNNs architectures it's natural to ask how to select the right architecture for your task. In a recent CVPR 2021 work propose a generic architecture that encompasses many existing GNNs, which is then optimized via gradient descent. After optimization resulted GNNs may get different architectures for each layer of GNNs.
Graph Machine Learning research groups: Yizhou Sun
I do a series of posts on the groups in graph research, previous post is here. The 28th is Yizhou Sun, a professor at UCLA, who co-authored a book on heterogeneous information networks.
Yizhou Sun (~1982)
- Affiliation: UCLA
- Education: Ph.D. at UIUC in 2012 (advisors: Jiawei Han)
- h-index 48
- Interests: heterogeneous information networks, self-supervised learning, community detection
- Awards: best research papers at KDD, ASONAM
I do a series of posts on the groups in graph research, previous post is here. The 28th is Yizhou Sun, a professor at UCLA, who co-authored a book on heterogeneous information networks.
Yizhou Sun (~1982)
- Affiliation: UCLA
- Education: Ph.D. at UIUC in 2012 (advisors: Jiawei Han)
- h-index 48
- Interests: heterogeneous information networks, self-supervised learning, community detection
- Awards: best research papers at KDD, ASONAM
Telegram
Graph Machine Learning
Graph Machine Learning research groups: Leman Akoglu
I do a series of posts on the groups in graph research, previous post is here. The 27th is Leman Akoglu, a professor at the Carnegie Mellon University, with interests in detecting anomalies in graphs.โฆ
I do a series of posts on the groups in graph research, previous post is here. The 27th is Leman Akoglu, a professor at the Carnegie Mellon University, with interests in detecting anomalies in graphs.โฆ
Mathematicians Answer Old Question About Odd Graphs
A new post at Quanta about the work that settles the question (c. 1960s) of the biggest subgraph with all vertices having odd degree within that subgraph.
A new post at Quanta about the work that settles the question (c. 1960s) of the biggest subgraph with all vertices having odd degree within that subgraph.
Quanta Magazine
Mathematicians Answer Old Question About Odd Graphs #separator_sa #site_title
A pair of mathematicians solved a legendary question about the proportion of vertices in a graph with an odd number of connections.
Fresh picks from ArXiv
This week on ArXiv: graph embeddings for drug discovery, new largest GNN, and a gym for solving combinatorial problems โน๏ธโ
If I forgot to mention your paper, please shoot me a message and I will update the post.
Drug discovery
* Predicting Potential Drug Targets Using Tensor Factorisation and Knowledge Graph Embeddings
* Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery with William L Hamilton
Software
* Dorylus: Affordable, Scalable, and Accurate GNN Training over Billion-Edge Graphs
* GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching
Combinatorics
* GraphSAT -- a decision problem connecting satisfiability and graph theory
* OpenGraphGym-MG: Using Reinforcement Learning to Solve Large Graph Optimization Problems on MultiGPU Systems
GNNs
* Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
Survey
* Federated Graph Learning -- A Position Paper
This week on ArXiv: graph embeddings for drug discovery, new largest GNN, and a gym for solving combinatorial problems โน๏ธโ
If I forgot to mention your paper, please shoot me a message and I will update the post.
Drug discovery
* Predicting Potential Drug Targets Using Tensor Factorisation and Knowledge Graph Embeddings
* Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery with William L Hamilton
Software
* Dorylus: Affordable, Scalable, and Accurate GNN Training over Billion-Edge Graphs
* GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching
Combinatorics
* GraphSAT -- a decision problem connecting satisfiability and graph theory
* OpenGraphGym-MG: Using Reinforcement Learning to Solve Large Graph Optimization Problems on MultiGPU Systems
GNNs
* Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
Survey
* Federated Graph Learning -- A Position Paper
NAACL-2021 Papers
A list of accepted papers to NLP conference NAACL-2021 is available at digest console. There are ~40 graph papers out of 476 papers.
A list of accepted papers to NLP conference NAACL-2021 is available at digest console. There are ~40 graph papers out of 476 papers.
TechViz - The Data Science Guy
A nice YouTube playlist explaining in details many works on graph embeddings.
A nice YouTube playlist explaining in details many works on graph embeddings.
YouTube
Anonymous Walk Embeddings | ML with Graphs (Research Paper Walkthrough)
#graphembedding #machinelearning #research
The research talks about using Random Walk inspired Anonymous Walks as graph units to derive feature-based and data-driven graph embeddings. Watch to know more :)
โฉ Abstract: The task of representing entire graphsโฆ
The research talks about using Random Walk inspired Anonymous Walks as graph units to derive feature-based and data-driven graph embeddings. Watch to know more :)
โฉ Abstract: The task of representing entire graphsโฆ
GNN User Group: meeting 5
Fifth meeting of GNN user group will include talks from:
* 4:00 - 4:25 (PST): Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration (Mahdi Saleh, TUM).
* 4:25 - 4:50 (PST): Optimizing Graph Transformer Networks with Graph-based Techniques (Loc Hoang, University of Texas at Austin)
* 4:50 - 5:15 (PST): Encoding the Core Business Entities Using Meituan Brain (Mengdi Zhang, Meituan)
* 5:15 - 5:30 (PST): Open Discussion and Networking
Please join us today, 27 May! Zoom link in the description.
Fifth meeting of GNN user group will include talks from:
* 4:00 - 4:25 (PST): Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration (Mahdi Saleh, TUM).
* 4:25 - 4:50 (PST): Optimizing Graph Transformer Networks with Graph-based Techniques (Loc Hoang, University of Texas at Austin)
* 4:50 - 5:15 (PST): Encoding the Core Business Entities Using Meituan Brain (Mengdi Zhang, Meituan)
* 5:15 - 5:30 (PST): Open Discussion and Networking
Please join us today, 27 May! Zoom link in the description.
Eventbrite
Graph Neural Networks User Group
Reinforcement learning for combinatorial optimization: A survey
Our work that surveys recent RL methods for solving combinatorial optimization problems is accepted at Computers & Operations Research journal.
This is very active field right now and it shows a lot of promise. Traditionally, NP-hard problems such as Traveling Salesman Problem were solved by algorithms, that were designed specifically for each problem. With RL, it's possible to extend the toolbox by learning a function on available data. I really hope that in 10 years from now using ML approaches for combinatorial problems will be a commonplace.
Our work that surveys recent RL methods for solving combinatorial optimization problems is accepted at Computers & Operations Research journal.
This is very active field right now and it shows a lot of promise. Traditionally, NP-hard problems such as Traveling Salesman Problem were solved by algorithms, that were designed specifically for each problem. With RL, it's possible to extend the toolbox by learning a function on available data. I really hope that in 10 years from now using ML approaches for combinatorial problems will be a commonplace.
Fresh picks from ArXiv
This week on ArXiv: equivariant GNNs to new groups, new metrics for graph similarity, and parsing emotions with GNNs ๐ขโ
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNNs
* How Attentive are Graph Attention Networks?
* Symmetry-driven graph neural networks
* Graph Similarity Description: How Are These Graphs Similar? KDD 2021
* SLGCN: Structure Learning Graph Convolutional Networks for Graphs under Heterophily
* Linguistic Structures as Weak Supervision for Visual Scene Graph Generation CVPR 2021
* Directed Acyclic Graph Network for Conversational Emotion Recognition ACL 2021
* On the Universality of Graph Neural Networks on Large Random Graphs
* Differentially Private Densest Subgraph Detection ICML 2021
Survey
* Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
* A Comprehensive Survey on Community Detection with Deep Learning
* A Survey of the Bridge Between Combinatorics and Probability
This week on ArXiv: equivariant GNNs to new groups, new metrics for graph similarity, and parsing emotions with GNNs ๐ขโ
If I forgot to mention your paper, please shoot me a message and I will update the post.
GNNs
* How Attentive are Graph Attention Networks?
* Symmetry-driven graph neural networks
* Graph Similarity Description: How Are These Graphs Similar? KDD 2021
* SLGCN: Structure Learning Graph Convolutional Networks for Graphs under Heterophily
* Linguistic Structures as Weak Supervision for Visual Scene Graph Generation CVPR 2021
* Directed Acyclic Graph Network for Conversational Emotion Recognition ACL 2021
* On the Universality of Graph Neural Networks on Large Random Graphs
* Differentially Private Densest Subgraph Detection ICML 2021
Survey
* Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
* A Comprehensive Survey on Community Detection with Deep Learning
* A Survey of the Bridge Between Combinatorics and Probability
Graph papers at ICML 2021
ICML 2021 papers are announced, here is some analysis on this.
There are about 58 graph papers (if I didn't mention your paper, let me know, I'll fix it).
The top authors are displayed.
ICML 2021 papers are announced, here is some analysis on this.
There are about 58 graph papers (if I didn't mention your paper, let me know, I'll fix it).
The top authors are displayed.
Almost Free Inductive Embeddings Out-Perform Trained Graph Neural Networks in Graph Classification in a Range of Benchmarks
A nice blog post by Vadym Safronov (in Russian also here) which shows that you can use not-trained GCN to match or exceed performance of end-to-end trained GCN on graph classification benchmarks.
A nice blog post by Vadym Safronov (in Russian also here) which shows that you can use not-trained GCN to match or exceed performance of end-to-end trained GCN on graph classification benchmarks.
Medium
Almost Free Inductive Embeddings Out-Perform Trained Graph Neural Networks in Graph Classification in a Range of Benchmarks
To train or not to trainโโโthat is not the question (Anonymous)
Graph Machine Learning research groups: Gal Chechik
I do a series of posts on the groups in graph research, previous post is here. The 29th is Gal Chechik, a professor at the Gonda Brain research institute and a director of AI at NVIDIA in Israel.
Gal Chechik (~1976)
- Affiliation: Bar Ilan University, Israel; NVIDIA
- Education: Ph.D. at Hebrew University, Israel in 2004 (advisors: Naftali Tishby and Israel Nelken)
- h-index 37
- Interests: biological systems, theory of GNNs, equivariant functions.
- Awards: best papers at ICML, ISMB; fullbright fellowship, Alon fellowship
I do a series of posts on the groups in graph research, previous post is here. The 29th is Gal Chechik, a professor at the Gonda Brain research institute and a director of AI at NVIDIA in Israel.
Gal Chechik (~1976)
- Affiliation: Bar Ilan University, Israel; NVIDIA
- Education: Ph.D. at Hebrew University, Israel in 2004 (advisors: Naftali Tishby and Israel Nelken)
- h-index 37
- Interests: biological systems, theory of GNNs, equivariant functions.
- Awards: best papers at ICML, ISMB; fullbright fellowship, Alon fellowship
Telegram
Graph Machine Learning
Graph Machine Learning research groups: Yizhou Sun
I do a series of posts on the groups in graph research, previous post is here. The 28th is Yizhou Sun, a professor at UCLA, who co-authored a book on heterogeneous information networks.
Yizhou Sun (~1982)โฆ
I do a series of posts on the groups in graph research, previous post is here. The 28th is Yizhou Sun, a professor at UCLA, who co-authored a book on heterogeneous information networks.
Yizhou Sun (~1982)โฆ