Graph Representation Learning for Drug Discovery Slides
Slides from Jian Tang of the talk on de novo drug discovery and drug repurposing.
Slides from Jian Tang of the talk on de novo drug discovery and drug repurposing.
GNN User Group Meeting 4 video
Video from the 4th meeting of GNN user group that includes talk from me (on GBDT+GNN model) and professor Pan Li on causal anonymous walks for temporal graphs. Slides can be found on DGL slack channel.
Video from the 4th meeting of GNN user group that includes talk from me (on GBDT+GNN model) and professor Pan Li on causal anonymous walks for temporal graphs. Slides can be found on DGL slack channel.
YouTube
Graph Neural Networks User Group Meeting on April 29, 2021
Agenda 4/29/2021:
4:00 - 4:30 (PST): Boost then Convolve: Gradient Boosting Meets Graph Neural Networks (Dr. Sergey Ivanov, Criteo, Russia).
4:30 - 5:00 (PST): Inductive Representation Learning of Temporal Networks via Causal Anonymous Walks (Prof. Pan Liโฆ
4:00 - 4:30 (PST): Boost then Convolve: Gradient Boosting Meets Graph Neural Networks (Dr. Sergey Ivanov, Criteo, Russia).
4:30 - 5:00 (PST): Inductive Representation Learning of Temporal Networks via Causal Anonymous Walks (Prof. Pan Liโฆ
Invariant and equivariant layers with applications to GNN, PointNet and Transformers
A blog post by Marc Lelarge about invariant and equivariant functions and their relation to the universality and expressivity of GNN. As the main result they show that any invariant/equivariant function on n points can be represented as a sum of functions on each point independently.
A blog post by Marc Lelarge about invariant and equivariant functions and their relation to the universality and expressivity of GNN. As the main result they show that any invariant/equivariant function on n points can be represented as a sum of functions on each point independently.
Fresh picks from ArXiv
This week on ArXiv: knowledge graph in production at Alibaba, links between WL and GNN, and more robust classifier with energy-based view ๐
If I forgot to mention your paper, please shoot me a message and I will update the post.
Applications
* Billion-scale Pre-trained E-commerce Product Knowledge Graph Model ICDE 2021
* Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph ICDE 2021
* Fast Multiscale Diffusion on Graphs
* An Energy-Based View of Graph Neural Networks
* Structure-Aware Hierarchical Graph Pooling using Information Bottleneck
* Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation
* Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network
Survey
* The Logic of Graph Neural Networks with Martin Grohe
* Graph Vulnerability and Robustness: A Survey
* Graph Learning: A Survey
* Graph Neural Networks for Traffic Forecasting
This week on ArXiv: knowledge graph in production at Alibaba, links between WL and GNN, and more robust classifier with energy-based view ๐
If I forgot to mention your paper, please shoot me a message and I will update the post.
Applications
* Billion-scale Pre-trained E-commerce Product Knowledge Graph Model ICDE 2021
* Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph ICDE 2021
* Fast Multiscale Diffusion on Graphs
* An Energy-Based View of Graph Neural Networks
* Structure-Aware Hierarchical Graph Pooling using Information Bottleneck
* Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation
* Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network
Survey
* The Logic of Graph Neural Networks with Martin Grohe
* Graph Vulnerability and Robustness: A Survey
* Graph Learning: A Survey
* Graph Neural Networks for Traffic Forecasting
Video: Workshop of Graph Neural Networks and Systems (GNNSys'21)
Very interesting videos from the workshop at MLSys 21 on GNNs in the industry. The talks include topics such as GNNs on graphcore's IPU, chip placement optimization, particle reconstruction at the large hadron collider and more.
Very interesting videos from the workshop at MLSys 21 on GNNs in the industry. The talks include topics such as GNNs on graphcore's IPU, chip placement optimization, particle reconstruction at the large hadron collider and more.
SlidesLive
MLSys 2021 | Workshop of Graph Neural Networks and Systems (GNNSys'21)
The Conference on Machine Learning and Systems targets research at the intersection of machine learning and systems. The conference aims to elicit new connections amongst these fields, including identifying best practices and design principles for learningโฆ
GML In-Depth: three forms of self-supervised learning
My new in-depth newsletter on self-supervised learning with applications to graphs. There is an upcoming keynote talk from Alexei Efros at ICLR'21 about self-supervised learning and I was inspired by the motivations that he talks there. In particular, he explains that self-supervised learning is a way to reduce the role of humans in designing ML pipelines, which would allow neural nets to learn in a similar way as humans do. Self-supervised learning for graphs is an active area of research and there are good reasons for this: for applications such as drug or catalyst discovery, there are billions of unlabeled graphs from which we would like to extract as much relevant information as possible. So self-supervised learning is becoming a new paradigm for learning such useful representations.
My new in-depth newsletter on self-supervised learning with applications to graphs. There is an upcoming keynote talk from Alexei Efros at ICLR'21 about self-supervised learning and I was inspired by the motivations that he talks there. In particular, he explains that self-supervised learning is a way to reduce the role of humans in designing ML pipelines, which would allow neural nets to learn in a similar way as humans do. Self-supervised learning for graphs is an active area of research and there are good reasons for this: for applications such as drug or catalyst discovery, there are billions of unlabeled graphs from which we would like to extract as much relevant information as possible. So self-supervised learning is becoming a new paradigm for learning such useful representations.
Graph Machine Learning
GML In-Depth: three forms of self-supervised learning
"Excelling at chess has long been considered a symbol of more general intelligence. That is an incorrect assumption in my view, as pleasant as it might be." Garry Kasparov
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