Graph Machine Learning

ICML GRL Workshop Papers There are 73 interesting short papers on various topics of GML at ICML GRL workshop.

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Graphs and Networks Workshop There is one-day free online workshop for those who love network science, happening this Friday, July 10.

Pytorch-Geometric version 1.6 In a new release PyG features support for static graph, a zoo of new models, and supplementary frameworks such as DeepSnap.

Fresh picks from ArXiv This week highlights clustering with GNN, scalable GNN, recommendation with graphs, and surveys on mathematical perspective of ML 💭 GNN • Graph Clustering with Graph Neural Networks with Anton Tsitsulin and Bryan Perozzi • Scaling Graph Neural Networks with Approximate PageRank with Bryan Perozzi, Stephan Günnemann, KDD 2020 • Simple and Deep Graph Convolutional Networks • AM-GCN: Adaptive Multi-channel Graph Convolutional Networks KDD 2020 • Adaptive Graph Encoder for Attributed Graph Embedding KDD 2020 • A Novel Higher-order Weisfeiler-Lehman Graph Convolution • Hierarchical Graph Matching Network for Graph Similarity Computation Applications • Disentangled Graph Collaborative Filtering SIGIR 2020 • Scene Graph Reasoning for Visual Question Answering with Stephan Günnemann • An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph with Alexander J. Smola, KDD 2020 • Interactive Path Reasoning on Graph for Conversational Recommendation KDD 2020 • New Hardness Results for Planar Graph Problems in P and an Algorithm for Sparsest Cut Survey Mathematical Perspective of Machine Learning Model-based Reinforcement Learning: A Survey Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey

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.

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.

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

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.

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)

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