# Machine Learning for Graphs/Networks

## Topics: Machine Learning for Graphs & Networks, Relational Learning, Deep Learning for Graphs, Graph Neural Networks, Network Analysis and Mining

One of the most common assumptions in many machine learning and data analysis tasks is that the given data points are realizations of independent and identically distributed random variables. This assumption, however, is often violated. Sensors are interlinked with each other in networked cyber physical systems, molecules or proteins interact based on biochemical events, and knowledge bases capture the relationships between different entities and concepts. Technically, in all these domains we have to deal with large-scale complex graphs/network.

In our research, we design machine learning and data analytics approaches which inherently capture the underlying dependence structure provided by the graph. In this regard, we cover a wide range of application use cases including novel models for node classification, graph clustering, anomaly detection, and graph generation and network inference. From a technical perspective, our methods often based on principles such as probabilistic modelling or neural networks.

** Deep Learning on Graphs:** While neural networks have achieved unprecedented performance on a variety of problems in different fields (e.g. computer vision and speech recognition), in the past, neural network architectures have focused on classical data domains (e.g. images, sequences). Recently the studies have been extended to the graph domain. Due to the unique characteristics of graphs (e.g. neighborhoods of varying size, long-range dependencies between nodes, sparsity), designing effective approaches, however, is highly challenging. In our research, we develop and analyze learning principles that enable to use neural networks in the context of graph data.

### Selected Publications:

Adversarial Attacks & Robustness: Are graph learning approaches robust? How to attack them? How to improve their robustness?

- Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, and Stephan Günnemann

**Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness**

*International Conference on Learning Representations (ICLR), 2022* - Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, and Stephan Günnemann

**Robustness of Graph Neural Networks at Scale**

*Neural Information Processing Systems (NeurIPS), 2021* - Simon Geisler, Daniel Zügner, Stephan Günnemann

**Reliable Graph Neural Networks via Robust Aggregation**

*Neural Information Processing Systems (NeurIPS), 2020* - Daniel Zügner, Stephan Günnemann

**Adversarial Attacks on Graph Neural Networks via Meta Learning**

*International Conference on Learning Representations (ICLR), 2019* - Daniel Zügner, Amir Akbarnejad, Stephan Günnemann

**Adversarial Attacks on Neural Networks for Graph Data (Best Research Paper Award)**

*ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018* - Aleksandar Bojchevski, Stephan Günnemann

**Adversarial Attacks on Node Embeddings**, Arxiv - Aleksandar Bojchevski, Stephan Günnemann

**Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure**

*AAAI Conference on Artificial Intelligence, 2018* - Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann

**Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings**

*ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017*

Neural Network Architectures for Graphs: How can we design novel neural network architectures that exploit the underlying properties of graphs? How can we capture relational dependencies to improve performance of learning?

- Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann

**Predict then Propagate: Graph Neural Networks meet Personalized PageRank**

*International Conference on Learning Representations (ICLR), 2019* - Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann

**Pitfalls of Graph Neural Network Evaluation**

*Relational Representation Learning Workshop, NIPS 2018* - Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein

**Dual-Primal Graph Convolutional Networks**, Arxiv - Aleksandar Bojchevski, Stephan Günnemann

**Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking**

*International Conference on Learning Representations (ICLR), 2018*

Graph Clustering / Segmentation / Community Detection: How do clusters/communities behave in real graphs? How to reliably and efficiently detect them?

- Saskia Metzler, Stephan Günnemann, Pauli Miettinen

**Stability and Dynamics of Communities on Online Question-Answer Sites**

*Social Networks, 2019* - Aleksandar Bojchevski, Stephan Günnemann

**Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure**

*AAAI Conference on Artificial Intelligence, 2018* - Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann

**Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings**

*ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017*

Generative Models for Graphs & Graph/Network Inference: How can we automatically generate graphs with realistic properties? Can we infer graphs/networks from other sources of information?

- Subhabrata Mukherjee and Stephan Günnemann

**GhostLink: Latent Network Inference for Influence-aware Recommendation**

*International World Wide Web Conference (WWW / TheWebConf), 2019* - Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

**NetGAN: Generating Graphs via Random Walks**

*International Conference on Machine Learning (ICML), 2018* - Aleksandar Bojchevski, Stephan Günnemann

**Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure**

*AAAI Conference on Artificial Intelligence, 2018*

Scalable Learning / Scalable Graph Analytics: How to ensure fast learning and analytics when operating with graph data?

- Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos

**The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation**

*SIAM International Conference on Data Mining (SDM), 2017* - Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos, Disha Makhija, Mohit Kumar

**ZooBP: Belief Propagation for Heterogeneous Networks**

*International Conference on Very Large Data Bases, PVLDB 10(5): 625-636 (2017)* - Manuel Then, Timo Kersten, Stephan Günnemann, Alfons Kemper, Thomas Neumann

**Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs**

*International Conference on Very Large Data Bases, PVLDB 10(8): 877-888 (2017)*