1 code implementation • 17 Jul 2024 • Salah Ghamizi, Aleksandar Bojchevski, Aoxiang Ma, Jun Cao

We provide at https://github. com/yamizi/SafePowerGraph our open-source repository, a comprehensive leaderboard, a dataset and model zoo and expect our framework to standardize and advance research in the critical field of GNN for power systems.

no code implementations • 12 Jul 2024 • Soroush H. Zargarbashi, Aleksandar Bojchevski

Conformal prediction (CP) transforms any model's output into prediction sets guaranteed to include (cover) the true label.

1 code implementation • 12 Jul 2024 • Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski

Conformal prediction (CP) can convert any model's output into prediction sets guaranteed to include the true label with any user-specified probability.

1 code implementation • 30 May 2024 • Vijay Lingam, Atula Tejaswi, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Alex Dimakis, Eunsol Choi, Aleksandar Bojchevski, Sujay Sanghavi

Extensive experiments on language and vision benchmarks show that SVFT recovers up to 96% of full fine-tuning performance while training only 0. 006 to 0. 25% of parameters, outperforming existing methods that only recover up to 85% performance using 0. 03 to 0. 8% of the trainable parameter budget.

no code implementations • NeurIPS 2023 • Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann

Randomized smoothing is a powerful framework for making models provably robust against small changes to their inputs - by guaranteeing robustness of the majority vote when randomly adding noise before classification.

1 code implementation • 7 Mar 2023 • Mohammad Sadegh Akhondzadeh, Vijay Lingam, Aleksandar Bojchevski

Our findings on molecular datasets show the potential of probing for understanding the inductive biases of graph-based models.

no code implementations • 6 Feb 2023 • Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann

In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a vector of labels) based on a single input, i. e. a single graph, image, or document respectively.

no code implementations • 31 Jan 2023 • Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski

A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs).

1 code implementation • 5 Jan 2023 • Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann

To remedy this, we propose novel gray-box certificates that exploit the message-passing principle of GNNs: We randomly intercept messages and carefully analyze the probability that messages from adversarially controlled nodes reach their target nodes.

1 code implementation • 9 Dec 2022 • Yihan Wu, Aleksandar Bojchevski, Heng Huang

In this paper, we extensively study this phenomenon for graph data.

no code implementations • 28 Oct 2022 • Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann

We further show that this approach is beneficial for the larger class of softly local models, where each output is dependent on the entire input but assigns different levels of importance to different input regions (e. g. based on their proximity in the image).

no code implementations • 15 Oct 2022 • Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie

A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if their distance is less than a specified neighborhood radius.

2 code implementations • NeurIPS 2021 • Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann

Graph Neural Networks (GNNs) are increasingly important given their popularity and the diversity of applications.

no code implementations • ICLR 2022 • Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann

Specifically, most datasets only capture a simpler subproblem and likely suffer from spurious features.

no code implementations • 29 Sep 2021 • Anna-Kathrin Kopetzki, Jana Obernosterer, Aleksandar Bojchevski, Stephan Günnemann

Our experiments show how adversarial training on the source domain affects robustness on source and target domain, and we propose the first provably robust transfer learning models.

no code implementations • ICLR 2021 • Jan Schuchardt, Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann

In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a vector of labels) based on a single input, i. e. a single graph, image, or document respectively.

1 code implementation • ICML 2020 • Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann

Existing techniques for certifying the robustness of models for discrete data either work only for a small class of models or are general at the expense of efficiency or tightness.

2 code implementations • 3 Jul 2020 • Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann

Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks.

1 code implementation • NeurIPS 2019 • Aleksandar Bojchevski, Stephan Günnemann

Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness.

no code implementations • ICLR 2019 • Aleksandar Bojchevski, Stephan Günnemann

The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks.

2 code implementations • 14 Nov 2018 • Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann

We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models.

5 code implementations • ICLR 2019 • Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann

We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP.

Ranked #1 on Node Classification on MS ACADEMIC

General Classification Node Classification on Non-Homophilic (Heterophilic) Graphs

1 code implementation • ICLR 2019 • Aleksandar Bojchevski, Stephan Günnemann

The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks.

no code implementations • 3 Jun 2018 • Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein

In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs.

2 code implementations • ICML 2018 • Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

NetGAN is able to produce graphs that exhibit well-known network patterns without explicitly specifying them in the model definition.

no code implementations • ICLR 2018 • Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

Moreover, GraphGAN learns a semantic mapping from the latent input space to the generated graph's properties.

1 code implementation • ICLR 2018 • Aleksandar Bojchevski, Stephan Günnemann

We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification.

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