KDD 2020: Workshop on Deep Learning on Graphs
If you miss ICML deadlines, there is another good workshop for GML at KDD.
Deadline: 15 June
5 pages, double-blind
If you miss ICML deadlines, there is another good workshop for GML at KDD.
Deadline: 15 June
5 pages, double-blind
Graph Representation Learning for Algorithmic Reasoning
Another idea coming more frequently in recent graph papers is to learn particular graph algorithm such as Bellman-Ford or Breadth-First Search, instead of doing node classification or link prediction. Here is a video from WebConf'20 by Petar VeliΔkoviΔ (DeepMind) motivating this approach.
Another idea coming more frequently in recent graph papers is to learn particular graph algorithm such as Bellman-Ford or Breadth-First Search, instead of doing node classification or link prediction. Here is a video from WebConf'20 by Petar VeliΔkoviΔ (DeepMind) motivating this approach.
YouTube
Graph Representation Learning for Algorithmic Reasoning
Slide deck: https://petar-v.com/talks/Algo-WWW.pdf
Graph Machine Learning research groups: Michael Bronstein
I do a series of posts on the groups in graph research. The fifth is Michael Bronstein. He founded a company Fabula AI on detecting fake news in social networks, which was acquired by Twitter. Also, he was a committee member of my PhD defense π
Michael Bronstein (1980)
- Affiliation: Imperial College London; Twitter
- Education: Ph.D. at Israel Institute of Technology in Israel in 2007 (supervised by Ron Kimmel);
- h-index: 61;
- Awards: IEEE and IARP fellow, Dalle Molle prize, Royal Society Wolfson Merit award;
- Interests: computer graphics, geometrical deep learning, graph neural networks.
I do a series of posts on the groups in graph research. The fifth is Michael Bronstein. He founded a company Fabula AI on detecting fake news in social networks, which was acquired by Twitter. Also, he was a committee member of my PhD defense π
Michael Bronstein (1980)
- Affiliation: Imperial College London; Twitter
- Education: Ph.D. at Israel Institute of Technology in Israel in 2007 (supervised by Ron Kimmel);
- h-index: 61;
- Awards: IEEE and IARP fellow, Dalle Molle prize, Royal Society Wolfson Merit award;
- Interests: computer graphics, geometrical deep learning, graph neural networks.
profiles.imperial.ac.uk
Michael Bronstein | About | Imperial College London
View the Imperial College London profile of Michael Bronstein. Including their publications and grants.
Max Welling Talk GNN
I recently thought about what are other types of GNN exist beyond message-passing. I think one of them can be equivariant networks, i.e. neural networks that have permutation-equivariant properties, but I think there are other possible powerful graph models that are yet to be discovered.
In this video, Max Welling discusses his recent works on equivariant NNs for meshes and factor GNNs.
I recently thought about what are other types of GNN exist beyond message-passing. I think one of them can be equivariant networks, i.e. neural networks that have permutation-equivariant properties, but I think there are other possible powerful graph models that are yet to be discovered.
In this video, Max Welling discusses his recent works on equivariant NNs for meshes and factor GNNs.
YouTube
MIT Talk GNNs May 08 2020
Injecting Inductive Bias in Graph Neural Networks:
Equivariant Mesh Neural Networks and Neural Augmented (Factor) Graph Neural Networks.
Co-authors Mesh-NNs: Pim de Haan, Maurice Weiler and Taco Cohen
Co-author Neural Augmented Factor Graph NNs: Victor Garciaβ¦
Equivariant Mesh Neural Networks and Neural Augmented (Factor) Graph Neural Networks.
Co-authors Mesh-NNs: Pim de Haan, Maurice Weiler and Taco Cohen
Co-author Neural Augmented Factor Graph NNs: Victor Garciaβ¦
Fresh picks from ArXiv
It's Tuesday and so it means we look back at the previous week of ArXiv. In today's episode, among most interesting papers, a new knowledge graph for PubMed π and new surveys on graph machine learning and quantum deep learning βοΈ
Applications
β’ Building a PubMed knowledge graph
β’ Reinforcement Learning with Feedback Graphs
β’ Predicting gene expression from network topology using graph neural networks
β’ On new record graphs close to bipartite Moore graphs
Conferences
β’ Bundle Recommendation with Graph Convolutional Networks SIGIR 20
β’ TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation SIGIR 20
β’ Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks IJCAI 20
β’ Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics ACL 20
Survey
β’ Machine Learning on Graphs: A Model and Comprehensive Taxonomy with Christopher RΓ©
β’ Comparison and Benchmark of Graph Clustering Algorithms
β’ Advances in Quantum Deep Learning: An Overview
It's Tuesday and so it means we look back at the previous week of ArXiv. In today's episode, among most interesting papers, a new knowledge graph for PubMed π and new surveys on graph machine learning and quantum deep learning βοΈ
Applications
β’ Building a PubMed knowledge graph
β’ Reinforcement Learning with Feedback Graphs
β’ Predicting gene expression from network topology using graph neural networks
β’ On new record graphs close to bipartite Moore graphs
Conferences
β’ Bundle Recommendation with Graph Convolutional Networks SIGIR 20
β’ TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation SIGIR 20
β’ Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks IJCAI 20
β’ Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics ACL 20
Survey
β’ Machine Learning on Graphs: A Model and Comprehensive Taxonomy with Christopher RΓ©
β’ Comparison and Benchmark of Graph Clustering Algorithms
β’ Advances in Quantum Deep Learning: An Overview
PhD Theses on Graph Machine Learning
Here are some PhD dissertations on GML (including mine).
Nino Shervashidze: Scalable graph kernels
Petar VeliΔkoviΔ: The resurgence of structure in deep neural networks
Sergei Ivanov: Combinatorial and neural graph vector representations
Thomas Kipf: Deep learning with graph-structured representations
Here are some PhD dissertations on GML (including mine).
Nino Shervashidze: Scalable graph kernels
Petar VeliΔkoviΔ: The resurgence of structure in deep neural networks
Sergei Ivanov: Combinatorial and neural graph vector representations
Thomas Kipf: Deep learning with graph-structured representations
dare.uva.nl
Digital Academic Repository - University of Amsterdam
Introduction to Deep Learning (I2DL)
There is a course on deep learning by Technical University of Munich. Recordings, slides, and exercises are available online.
There is a course on deep learning by Technical University of Munich. Recordings, slides, and exercises are available online.
Secrets of the Surface: The Mathematical Vision of Maryam Mirzakhani
There is a documentary that you can watch on the life of Maryam Mirzakhani. In 2014, she was awarded the Fields medal for the work in "the dynamics and geometry of Riemann surfaces and their moduli spaces." You can read about her in this article. For the film, you can register here and they will send a link to the Vimeo, which will be available until 19th May.
There is a documentary that you can watch on the life of Maryam Mirzakhani. In 2014, she was awarded the Fields medal for the work in "the dynamics and geometry of Riemann surfaces and their moduli spaces." You can read about her in this article. For the film, you can register here and they will send a link to the Vimeo, which will be available until 19th May.
Quanta Magazine
A Tenacious Explorer of Abstract Surfaces
Maryam Mirzakhani, who became the first woman Fields medalist for drawing deep connections between topology, geometry and dynamical systems, has died of cancer at the age of 40. This is our 2014β¦
AI and Theorem Proving
One of the topics that caught my attention was on using AI to automate theorem proving. Apparently, there is already a conference on this. At ICLR there was a paper on using graph networks for theorem proving.
I think besides this conference, which mainly explores how you can model mathematical logic using embeddings, another type of theorem proving is on smart pruning of combinatorial spaces (e.g. you have large space of graphs, from which you need to pick some particular examples).
One of the topics that caught my attention was on using AI to automate theorem proving. Apparently, there is already a conference on this. At ICLR there was a paper on using graph networks for theorem proving.
I think besides this conference, which mainly explores how you can model mathematical logic using embeddings, another type of theorem proving is on smart pruning of combinatorial spaces (e.g. you have large space of graphs, from which you need to pick some particular examples).
Learning graph structure to help classification
I just recently discussed an idea whether it's possible to create a graph from a non-graph classification data set and improve classification performance by doing it and I found two works on it.
First approach just tries different values for knn to connect the points into a graph, obtains a graph for each parameter setting, and verifies the performance of classification of a graph model on the obtained graph. Clearly the problem with it is that you have to do classification many many times for different parameters of your knn.
Second approach (ICML 2019, link to presentation) is more data-driven: instead of freezing the parameters of knn, it uses a graph generative model that would generate a graph from the points. Then a graph neural network would make a classification prediction and, together with parameters of generative model, would be updated by backpropagation. It's still quite heavy as you need to update parameters of two different models instead of one. Perhaps, there are future works that would create the graphs at little computational cost and would boost the results for classification pipelines.
I just recently discussed an idea whether it's possible to create a graph from a non-graph classification data set and improve classification performance by doing it and I found two works on it.
First approach just tries different values for knn to connect the points into a graph, obtains a graph for each parameter setting, and verifies the performance of classification of a graph model on the obtained graph. Clearly the problem with it is that you have to do classification many many times for different parameters of your knn.
Second approach (ICML 2019, link to presentation) is more data-driven: instead of freezing the parameters of knn, it uses a graph generative model that would generate a graph from the points. Then a graph neural network would make a classification prediction and, together with parameters of generative model, would be updated by backpropagation. It's still quite heavy as you need to update parameters of two different models instead of one. Perhaps, there are future works that would create the graphs at little computational cost and would boost the results for classification pipelines.
Fresh picks from ArXiv
This week highlights papers on scene graph generation, isomorphism testing by GNNs and WL, and weight estimation of pork cuts π
GNN
β’ How hard is graph isomorphism for graph neural networks? by Andreas Loukas
β’ Neural Stochastic Block Model & Scalable Community-Based Graph Learning
β’ Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
β’ Structured Query-Based Image Retrieval Using Scene Graphs
β’ Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
β’ SpectralWeight: a spectral graph wavelet framework for weight prediction of pork cuts
Conferences
β’ Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection ACL 20
β’ GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions IJCAI 20
β’ Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension ACL 20
Graph Theory
β’ Acyclic edge coloring conjecture is true on planar graphs without intersecting triangles
β’ The Weifeiler-Leman Algorithm and Recognition of Graph Properties
Surveys
β’ Visual Relationship Detection using Scene Graphs: A Survey
β’ Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
β’ Recent Advances in SQL Query Generation: A Survey
β’ Explainable Reinforcement Learning: A Survey
This week highlights papers on scene graph generation, isomorphism testing by GNNs and WL, and weight estimation of pork cuts π
GNN
β’ How hard is graph isomorphism for graph neural networks? by Andreas Loukas
β’ Neural Stochastic Block Model & Scalable Community-Based Graph Learning
β’ Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
β’ Structured Query-Based Image Retrieval Using Scene Graphs
β’ Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
β’ SpectralWeight: a spectral graph wavelet framework for weight prediction of pork cuts
Conferences
β’ Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection ACL 20
β’ GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions IJCAI 20
β’ Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension ACL 20
Graph Theory
β’ Acyclic edge coloring conjecture is true on planar graphs without intersecting triangles
β’ The Weifeiler-Leman Algorithm and Recognition of Graph Properties
Surveys
β’ Visual Relationship Detection using Scene Graphs: A Survey
β’ Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
β’ Recent Advances in SQL Query Generation: A Survey
β’ Explainable Reinforcement Learning: A Survey
ACL 2020 stats
ACL (Association for Computational Linguistics) is the top conferences in NLP. Each year there are more and more papers that use graphs with natural language.
Dates: July 5-10
Where: Online
β’ 3088 submissions (2906 submissions in 2019)
β’ 571/208 long and short papers (25% overall acceptance rate; 447/213 in 2019)
β’ 42/6 long/short graph papers (7%/3% of total)
ACL (Association for Computational Linguistics) is the top conferences in NLP. Each year there are more and more papers that use graphs with natural language.
Dates: July 5-10
Where: Online
β’ 3088 submissions (2906 submissions in 2019)
β’ 571/208 long and short papers (25% overall acceptance rate; 447/213 in 2019)
β’ 42/6 long/short graph papers (7%/3% of total)
ACL 2020
The 58th Annual Meeting of the Association for Computational Linguistics
CVPR 2020
Computer Vision and Pattern Recognition (CVPR) conference is the top conference in CV and has had increasing focus on application of graphs to images. Here are some facts about CVPR 2020.
Papers link
Dates: June 14-19
Where: Virtual
β’ 6,656 total papers
β’ 1,470 accepted papers
β’ 22% acceptance rate
β’ ~69 graph papers (~5% of total)
Computer Vision and Pattern Recognition (CVPR) conference is the top conference in CV and has had increasing focus on application of graphs to images. Here are some facts about CVPR 2020.
Papers link
Dates: June 14-19
Where: Virtual
β’ 6,656 total papers
β’ 1,470 accepted papers
β’ 22% acceptance rate
β’ ~69 graph papers (~5% of total)
Graph Machine Learning research groups: Christos Faloutsos
I do a series of posts on the groups in graph research. The sixth is Christos Faloutsos. He was an advisor for many current professors in GML such as Jure Leskovec, Leman Akoglu, Stephan Guennemman, and Bruno Ribeiro.
Christos Faloutsos (~1960)
- Affiliation: Carnegie Mellon University; Amazon
- Education: Ph.D. at University of Toronto in 1987 (supervised by Stavros Christodoulakis);
- h-index: 131;
- Awards: ACM fellow, best paper awards at KDD, SIGMOD, ICDM;
- Interests: data mining; database; anomaly detection in graphs.
I do a series of posts on the groups in graph research. The sixth is Christos Faloutsos. He was an advisor for many current professors in GML such as Jure Leskovec, Leman Akoglu, Stephan Guennemman, and Bruno Ribeiro.
Christos Faloutsos (~1960)
- Affiliation: Carnegie Mellon University; Amazon
- Education: Ph.D. at University of Toronto in 1987 (supervised by Stavros Christodoulakis);
- h-index: 131;
- Awards: ACM fellow, best paper awards at KDD, SIGMOD, ICDM;
- Interests: data mining; database; anomaly detection in graphs.
Social network data set
Anton @xgfsru shared a data set VK1M (password 1234), with first 1M users from social network vk.com (data is taken via public API). In addition to the friends of each user, the file contains meta-information such as education, country, birthday of each node. It can be useful for node classification or regression tasks as well as community or anomaly detection.
Anton @xgfsru shared a data set VK1M (password 1234), with first 1M users from social network vk.com (data is taken via public API). In addition to the friends of each user, the file contains meta-information such as education, country, birthday of each node. It can be useful for node classification or regression tasks as well as community or anomaly detection.
mega.nz
File on MEGA
Fresh picks from ArXiv
This week has more papers from upcoming ACL, SIGIR, and KDD; a new survey on combinatorial optimization on graphs; and Borsuk's conjecture πΉ
Conferences
β’ Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach SIGIR 20
β’ ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation SIGIR 20
β’ Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks KDD 20
β’ Understanding Negative Sampling in Graph Representation Learning KDD 20
β’ Leveraging Graph to Improve Abstractive Multi-Document Summarization ACL 20
β’ M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems KDD 20
β’ Graph Structure Learning for Robust Graph Neural Networks KDD 20
Surveys
β’ Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
β’ How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
β’ Motif Discovery Algorithms in Static and Temporal Networks: A Survey
Graph Theory
β’ The Weisfeiler-Leman dimension of distance-hereditary graphs
β’ Counterexamples to Borsuk's conjecture from a third strongly regular graph
β’ A Group-Theoretic Framework for Knowledge Graph Embedding
This week has more papers from upcoming ACL, SIGIR, and KDD; a new survey on combinatorial optimization on graphs; and Borsuk's conjecture πΉ
Conferences
β’ Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach SIGIR 20
β’ ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation SIGIR 20
β’ Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks KDD 20
β’ Understanding Negative Sampling in Graph Representation Learning KDD 20
β’ Leveraging Graph to Improve Abstractive Multi-Document Summarization ACL 20
β’ M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems KDD 20
β’ Graph Structure Learning for Robust Graph Neural Networks KDD 20
Surveys
β’ Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
β’ How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
β’ Motif Discovery Algorithms in Static and Temporal Networks: A Survey
Graph Theory
β’ The Weisfeiler-Leman dimension of distance-hereditary graphs
β’ Counterexamples to Borsuk's conjecture from a third strongly regular graph
β’ A Group-Theoretic Framework for Knowledge Graph Embedding
How hard is graph isomorphism for graph neural networks?
This is a new work by Andreas Loukas that sheds a little bit more light into the theory behind GNN. The analysis relies on the amount of information nodes should exchange in order to detect isomorphism class of each graph. This problem of finding isomorphism class is called graph canonization problem, which is probably even harder than graph isomorphism. As such a GNN model needs to output a number for each possible graph up to isomorphism, and needless to say the number of non-isomorphic graphs grows closely to factorial terms. Hence the experiments, while support the theory, are done only on very small graphs ~10 nodes.
This is a new work by Andreas Loukas that sheds a little bit more light into the theory behind GNN. The analysis relies on the amount of information nodes should exchange in order to detect isomorphism class of each graph. This problem of finding isomorphism class is called graph canonization problem, which is probably even harder than graph isomorphism. As such a GNN model needs to output a number for each possible graph up to isomorphism, and needless to say the number of non-isomorphic graphs grows closely to factorial terms. Hence the experiments, while support the theory, are done only on very small graphs ~10 nodes.
Wikipedia
Graph canonization
computational problem
On the universal equivariant functions
Yesterday's post was followed by a conversation with Andreas Loukas on the power of graph neural networks. There is one detail about the current analysis of GNN, which I didn't pay much attention before, even though I encountered it (it's a recurring phenomenon for me, when some major insight is given one sentence in the paper and is not highlighted in CAPITAL letters).
The insight is that there are two types of GNN, anonymous and non-anonymous. Anonymous case means you are invariant to the order of nodes, for example when you use the sum aggregation over nodes. It was shown that anonymous case is equivalent to WL algorithm and therefore has a lot limitations such as not being able to count subgraphs or distinguish graphons, etc. So the current anonymous models are not universal: they cannot compute all the functions of the inputs. It's a weaker model than non-anonymous case, when you give some orderings to the nodes and then you iterate over all orderings.
Non-anonymous models have additional node features, for example one-hot encodings of their position in adjacency matrix, which is one of the sufficient conditions for GNN to be universal. There are then two scenarios. Either you consider all possible permutations, in which case it grows in factorial terms and essentially it's a cheat. Or you resort to a single permutation, but then do not enjoy the invariance property of GNN, i.e. for different orderings it can give different set of embeddings for the same nodes.
So it's interesting to see if there are universal equivariant functions that do not use all node permutations, which is still an open question.
Yesterday's post was followed by a conversation with Andreas Loukas on the power of graph neural networks. There is one detail about the current analysis of GNN, which I didn't pay much attention before, even though I encountered it (it's a recurring phenomenon for me, when some major insight is given one sentence in the paper and is not highlighted in CAPITAL letters).
The insight is that there are two types of GNN, anonymous and non-anonymous. Anonymous case means you are invariant to the order of nodes, for example when you use the sum aggregation over nodes. It was shown that anonymous case is equivalent to WL algorithm and therefore has a lot limitations such as not being able to count subgraphs or distinguish graphons, etc. So the current anonymous models are not universal: they cannot compute all the functions of the inputs. It's a weaker model than non-anonymous case, when you give some orderings to the nodes and then you iterate over all orderings.
Non-anonymous models have additional node features, for example one-hot encodings of their position in adjacency matrix, which is one of the sufficient conditions for GNN to be universal. There are then two scenarios. Either you consider all possible permutations, in which case it grows in factorial terms and essentially it's a cheat. Or you resort to a single permutation, but then do not enjoy the invariance property of GNN, i.e. for different orderings it can give different set of embeddings for the same nodes.
So it's interesting to see if there are universal equivariant functions that do not use all node permutations, which is still an open question.
Other telegram channels
There are many other interesting channels, below are English-speaking that I follow. If I miss something valuable, feel free to send them to me and I will update the post.
https://t.iss.one/opendatascience
https://t.iss.one/ArtificialIntelligencedl
https://t.iss.one/ai_machinelearning_big_data
https://t.iss.one/j_links
https://t.iss.one/ArtificialIntelligenceArticles
https://t.iss.one/snakers4
https://t.iss.one/loss_function_porn
https://t.iss.one/yegor256news
Also quite useful aggregator https://infomate.club/ml/
There are many other interesting channels, below are English-speaking that I follow. If I miss something valuable, feel free to send them to me and I will update the post.
https://t.iss.one/opendatascience
https://t.iss.one/ArtificialIntelligencedl
https://t.iss.one/ai_machinelearning_big_data
https://t.iss.one/j_links
https://t.iss.one/ArtificialIntelligenceArticles
https://t.iss.one/snakers4
https://t.iss.one/loss_function_porn
https://t.iss.one/yegor256news
Also quite useful aggregator https://infomate.club/ml/
Telegram
Data Science by ODS.ai π¦
First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. To reach editors contact: @malev
Why `True is False is False` -> False?
Stumbled upon this little question why
Stumbled upon this little question why
True is False is False evaluates to False in python. The answer is as simple as the question, but not obvious and many people could not answer it. So I wrote a quick post about it.Medium
Why `True is False is False` -> False?
Here is a little interview question to you, experienced Python programmer.
Fresh picks from ArXiv
This week has GNN variants for various types of graphs, NP-completeness of MaxCut problem, and a survey on graph data management π₯
GNN
β’ Understanding the Message Passing in Graph Neural Networks via Power Iteration
β’ Interpretable and Efficient Heterogeneous Graph Convolutional Network
β’ Graph Neural Network for Hamiltonian-Based Material Property Prediction
β’ Non-IID Graph Neural Networks
β’ Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
β’ Non-Local Graph Neural Networks
Graph Theory
β’ Complexity of Maximum Cut on Interval Graphs
β’ Planar Graphs that Need Four Pages
Surveys
β’ Benchmarking Graph Data Management and Processing Systems: A Survey
This week has GNN variants for various types of graphs, NP-completeness of MaxCut problem, and a survey on graph data management π₯
GNN
β’ Understanding the Message Passing in Graph Neural Networks via Power Iteration
β’ Interpretable and Efficient Heterogeneous Graph Convolutional Network
β’ Graph Neural Network for Hamiltonian-Based Material Property Prediction
β’ Non-IID Graph Neural Networks
β’ Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
β’ Non-Local Graph Neural Networks
Graph Theory
β’ Complexity of Maximum Cut on Interval Graphs
β’ Planar Graphs that Need Four Pages
Surveys
β’ Benchmarking Graph Data Management and Processing Systems: A Survey