Graph Machine Learning

KDD deadline is coming next week and it is one of the most popular places to submit strong GML paper, even though it is a general data mining conference, with all sorts of papers in computer science. 4 out of 5 last years, the best papers were assigned to graph research. 2019 Network Density of States https://www.kdd.org/kdd2019/accepted-papers/view/network-density-of-states 2018 Adversarial Attacks on Neural Networks for Graph Data https://www.kdd.org/kdd2018/accepted-papers/view/adversarial-attacks-on-neural-networks-for-graph-data 2016 FRAUDAR: Bounding Graph Fraud in the Face of Camouflage https://www.kdd.org/kdd2016/subtopic/view/fraudar-bounding-graph-fraud-in-the-face-of-camouflage 2015 Efficient Algorithms for Public-Private Social Networks https://ai.googleblog.com/2015/08/kdd-2015-best-research-paper-award.html

Two tutorials on GML at AAAI 2020. Graph Neural Networks: Models and Applicationshttps://aaai.org/Conferences/AAAI-20/aaai20tutorials/#fa4 Differential Deep Learning on Graphs and its Applicationshttps://aaai.org/Conferences/AAAI-20/aaai20tutorials/#fp1

AAAI 2020 is taking place tomorrow in NYC. AAAI 2020 stats 7737 number of submissions 1591 number of accepted 21% acceptance rate 142 graph accepted papers (9% of total) ICLR 2020 stats 2213 number of submissions 687 number of accepted 31% acceptance rate 49 graph accepted papers (7% of total)

Our new submission for ICLR workshop on AI+Neuroscience: https://baicsworkshop.github.io/ Here is what I think. 1️⃣ Even for simple ideas it takes still 5-7 days to implement and write 4 pages. 2️⃣ More importantly, this work is about prediction of IQ based on the EEG brain measurements. Essentially, here is (X, y) train whatever model you want and report the result. The problem is that for real data sets simple baselines work better than your machine learning. For example, taking the most common y from the training set and predicting it for all examples test gives the result very close to ML. It would be cool to have some hints from the oracle that would say "Don't bother, these data are doomed, you can't do better with ML". If you know some theory like that, please ping me in private messages 🙁

One of the trends that I outlined in the post above was about the growing rate of papers on knowledge graphs. This is quite interesting as I realize that many recommendation tasks for example in conversational AI systems can be modeled well with knowledge graphs, instead of let's say deep learning methods. Here is a very fresh survey on this topic. A Survey on Knowledge Graphs: Representation, Acquisition and Applications. http://arxiv.org/abs/2002.00388

Finally finished the post on the cutting-edge research in Graph Machine Learning. A lot of interesting ideas and applications. https://towardsdatascience.com/top-trends-of-graph-machine-learning-in-2020-1194175351a3

There are quite a few tools to monitor new papers on ArXiv: * arxiv-sanity.com * arxivist.com But you can also configure rss feed on the keywords that you like by using https://siftrss.com/ For example, if you want papers onlyon graphs you can use the following links: CS track: https://siftrss.com/f/x70NM5NWmLn Stat track: https://siftrss.com/f/3meBo55VMyA

Continuing this, a group of Le Song has 7 papers at ICLR, all accepts. This is top-2 result among all, with the first one Sergey Levine having 13 accepts. 1. HOPPITY: Learning Graph Transformations to Detect and Fix Bugs in Programs (https://openreview.net/forum?id=SJeqs6EFvB) 2. GLAD: Learning Sparse Graph Recovery (https://openreview.net/forum?id=BkxpMTEtPB) 3. Efficient Probabilistic Logic Reasoning with Graph Neural Networks (https://openreview.net/forum?id=rJg76kStwH) 4. Double Neural Counterfactual Regret Minimization (https://openreview.net/forum?id=ByedzkrKvH) 5. RNA Secondary Structure Prediction By Learning Unrolled Algorithms (https://openreview.net/forum?id=S1eALyrYDH) 6. Learn to Explain Efficiently via Neural Logic Inductive Learning (https://openreview.net/forum?id=SJlh8CEYDB) 7. Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees (https://openreview.net/forum?id=rJgJDAVKvB)

6 papers at ICLR by the group of Jure Leskovec (3 accepts + 3 rejects) 1. Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings (https://openreview.net/forum?id=BJgr4kSFDS) 2. Strategies for Pre-training Graph Neural Networks (https://openreview.net/forum?id=HJlWWJSFDH) 3. Redundancy-Free Computation Graphs for Graph Neural Networks (https://openreview.net/forum?id=H1eF3kStPS) 4. Unifying Graph Convolutional Neural Networks and Label Propagation (https://openreview.net/forum?id=rkgdYhVtvH) 5. Selection via Proxy: Efficient Data Selection for Deep Learning (https://openreview.net/forum?id=HJg2b0VYDr) 6. Coresets for Accelerating Incremental Gradient Methods (https://openreview.net/forum?id=SygRikHtvS)

There is a recent trend in machine learning papers to do ablation studies, showing that SOTA results are not that great compared to old baselines. RecSys 19 best paper was about it (https://arxiv.org/abs/1907.06902). I think I saw some similar works in NLP and CV, and now it's time for GML. Two papers, one in knowledge graph link prediction and another in graph classification: https://openreview.net/forum?id=BkxSmlBFvr https://openreview.net/forum?id=HygDF6NFPB

How Uber Eats uses GNNs to power recommendations. https://eng.uber.com/uber-eats-graph-learning/