PhD Theses on Graph Machine Learning

Here are some PhD dissertations on GML. Part 2 (previous here).

Haggai Marron: Deep and Convex Shape Analysis

Benoit Playe: Machine learning approaches for drug virtual screening

Graph Machine Learning research groups: Tommi Jaakkola

I do a series of posts on the groups in graph research, previous post is here. The eighth is Tommi Jaakkola. He has 7 papers in upcoming ICML 2020. His recent interests include molecular graph design and he maintains AI initiative for finding promising antiviral molecules for COVID-19.


Tommi Jaakkola (~1971)
- Affiliation: MIT
- Education: Ph.D. at MIT in 1997 (supervised by Michael Jordan);
- h-index: 76;
- Awards: Sloan research fellowship, AAAI Fellow;
- Interests: molecular generation, models of GNN

DeepSnap

There is a release of DeepSnap by Stanford group. I have not tested it, but it should allow applying graph algorithms from networkx to pytorch-geometric graphs.

Implicit Neural Representations by Yaron Lipman

The talk Implicit Neural Representations by Yaron Lipman from CVPR 20 workshop on Deep Learning Foundations of Geometric Shape.

Fresh picks from ArXiv
This week highlights applications of GNNs to molecules, contagion, NLP, recommender systems and more.

GNN
• Generalizing Graph Neural Networks Beyond Homophily
• Finding Patient Zero: Learning Contagion Source with Graph Neural Networks with Albert-László Barabási
• MoFlow: An Invertible Flow Model for Generating Molecular Graphs
• Quantifying Challenges in the Application of Graph Representation Learning
• Neural Architecture Optimization with Graph VAE
• Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
• Subgraph Neural Networks with Marinka Zitnik
• Temporal Graph Networks for Deep Learning on Dynamic Graphs with Michael Bronstein
• Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs with Andreas Loukas
• Walk Message Passing Neural Networks and Second-Order Graph Neural Networks
• Isometric Graph Neural Networks
• Modeling Graph Structure via Relative Position for Better Text Generation from Knowledge Graphs
• Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting with Michael Bronstein

Math:
• Local limit theorems for subgraph counts
• Longest and shortest cycles in random planar graphs


Conferences
• How to Count Triangles, without Seeing the Whole Graph KDD 2020
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training KDD 2020

Surveys
• Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison

Spektral

Spektral is a library to code GNN in Tensorflow 2 and Keras. New version includes:

- a unified message-passing interface based on gather-scatter
- 7 new GNN layers
- Huge performance improvements
- Improved utils, docs, and examples

The paper will be presented in GRL workshop.

Criteo papers at ICML 2020

Criteo, where I work, this year has record number of accepted papers at ICML. We have 9 papers on various topics, from online learning to theory of optimization to GANs. It makes us 1st company in EU and top-7 company worldwide (among 134 companies who have their papers accepted). So I wrote a short description of each paper in a new blog post.

Top number of submissions at NeurIPS 2020

Mastodons of ML are the following:

* Peter Richtárik (KAUST) 14
* Bernhard Schölkopf (MPI) 13
* Sergey Levine (UC Berkeley) 12
* Masashi Sugiyama (RIKEN) 11
* Yoshua Bengio (MILA) 11

This is based on 2313 arXiv papers that are submitted to NeurIPS2020.

Last year there were at least some people with 15 submissions, so it's probably underestimates these numbers. Also, compared to last year there was 54% of the papers appearing in arXiv at the moment of the conference. For this year, today there are 25% of arXiv papers, so it means not everyone submitted their papers to arXiv.

Sylow theorems and algebraic geometry

There is a fresh thread on Sylow theorems, which are popular results in group theory. I'm not sure how much the waste of time is studying group theory, that's something in my todo list, but this thread is giving a good intro to it.

Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework

This is a post by Michael Galkin (@gimmeblues) about their new work on comprehensive evaluation of knowledge graph embeddings. A lot of interesting insights about knowledge graphs.

Today we are publishing the results of our large-scale benchmarking study of knowledge graph (KG) embedding approaches. Further, we are releasing the code of PyKEEN 1.0 - the library behind the study (in PyTorch)! What makes KGs special: they often have hundreds or thousands of different relations (edge types), and having good representations is essential for reasoning in embedding spaces as well as for numerous NLP tasks.

We often evaluate KG embeddings on the link prediction task - given subject+predicate, the model has to predict most plausible objects. As typical KGs contain 50k-100k different entities, you can guess the top1/top10 ranking task is quite complex!

Why benchmarking is important: currently, there is no baseline numbers to refer to. Lots of papers in the domain are not reproducible, or the authors simply take metrics values as reported in other papers withougt reproducing their results.

In this study, we ran 65K+ experiments and spent 21K+ GPU hours evaluating 19 models spanning from RESCAL first published in 2011 to the late 2019's RotatE and TuckER, 5 loss functions, training strategies with/without negative sampling, and many more hyper-parameters that turn out to be important to consider.

Key findings:
- Careful HPO optimization brings us new SOTA results giving significant gains of 4-5% compared to reported results in respective papers (btw, we used Optuna for HPO);
- Properly tuned classical models (TransE, DistMult) are still good and actually outperform several newer models;
- No Best-of-the-Best Silver Bullet model that beats all others across all tasks - some models better capture transitivity, whereas other better capture symmetric relations;
- Surprisingly, for the inherently ranking task, the ranking loss (or MarginRankingLoss in PyTorch) is suboptimal. Instead, Cross-Entropy and its variations show better result;
- Using all enities for negative sampling, i.e., sigmoid/softmax distribution over all enities, works well but can be quite expensive on large KGs. Stochastic negative sampling is a way to go then;
- Computationally expensive and bigger models do not yield that big and drastic performance gains. In fact, 64-d Rotate is better than most 500-d models.


Paper: https://arxiv.org/abs/2006.13365
Code: https://github.com/pykeen/pykeen

Machine Learning Summer School 2020

Today starts Machine Learning Summer School 2020 with great list of speakers on various topics of machine learning, including geometric deep learning. The lectures will be live-streamed on YouTube and are open to everyone.

Manually-curated List of Combinatorial Conferences

Mostly mathematical, with some occasions on CS, here is a manually-curated list of upcoming conferences, workshops, symposiums on combinatorics, among which you can find graph-related topics.

Fresh picks from ArXiv
This week we have papers on theory of GNN, their applications to recommendation and other fields, a historical reference on available graph repositories, and a discussion of peer review vs biblio metrics to assess scientific performance 👨‍⚖️

GNN
• Characterizing the Expressive Power of Invariant and Equivariant Graph Neural Networks
• Building powerful and equivariant graph neural networks with message-passing with Andreas Loukas
• Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case ICML 2020

Applications
• Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters
• Scalable Deep Generative Modeling for Sparse Graphs with Yujia Li, ICML 2020
• GPT-GNN: Generative Pre-Training of Graph Neural Networks KDD 2020
• Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits

Graph Problems
• Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View with Philip S. Yu
• Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework with Mikhail Galkin
• Graph Policy Network for Transferable Active Learning on Graphs with Jian Tang
• Online Dense Subgraph Discovery via Blurred-Graph Feedback with Masashi Sugiyama, ICML 2020

Surveys
• A survey of repositories in graph theory
• Metrics and peer review agreement at the institutional level

UAI 2020 stats

UAI is a small but strong conference on AI.

Dates: 3-6 Aug
Where: Online
Cost: 125$
Papers available online.

• 580 submissions (vs 450 in 2019)
• 140 accepted (vs 118 in 2019)
• 24.1% acceptance rate (vs 26% in 2019)
• 5 graph papers (4% of total)

Open Problems - Graph Theory and Combinatorics

In addition to Open Problem Garden, there is a list of open problems in graph theory and a corresponding old archive. Sometimes proof to these is just a specific graph that even people without background may find.

Graph Machine Learning research groups: Stephan Günnemann

I do a series of posts on the groups in graph research, previous post is here. The nineth is Stephan Günnemann. His research interests include adversarial attacks on graphs and graph generative models.


Stephan Günnemann (~1984)
- Affiliation: Technical University of Munich
- Education: Ph.D. at RWTH Aachen University in 2012 (supervised by Thomas Seidl);
- h-index: 30;
- Awards: best paper at KDD;
- Interests: graph adversarial attacks; clustering; graph generative models

Graphs with the same degree distribution

Degree distribution plays a key distinctive role between graphs. In networks science there are specific models that generate you a graph according to some distribution of degrees. For example, scale-free networks are the ones with power law degree distribution, which we observe in real world (e.g. social networks). Scale-free networks use preferential attachment mechanism that mimics the way people connect with others in a new society: we connect to people with high degree and people that we know. The Barabási–Albert model is the most famous example of such a model.

What's interesting in some cases is to provide explicitly the degrees that you expect to have in a graph and then generate a graph with this sequence of degrees. There is a model for that too: it's called Chung-Lu model. Yet, in some other cases, you want to generate a graph exactly with some degree sequence. This is quite simple, you just connect pairs of vertices one by one, until you make a desired degree sequence. It shows how many actually there are different graphs with the same degree sequence. Here is an explanation of this.

Beyond Weisfeiler-Lehman: using substructures for provably expressive graph neural networks

This is the second post by Michael Bronstein, where he discussed his recent architecture of GNN. In one sentence, they append information about graph statistics, such as number of 4-cliques, to message-passing mechanism and show that it is theoretically equivalent to k-WL, with fraction of its cost.

For more than 6 months, I wondered why do we try to design GNN that can solve graph isomorphism (GI), if in all cases we are at most as good as already known algorithms to GI. What if we just take a automorphism group of a graph and then append this information to GNN, hoping it will help for downstream tasks. This way we solve GI by default by using automorphism group, and just measure effectiveness of the GNN for the tasks that matter.