Search Results for author: Christopher Morris

Found 31 papers, 18 papers with code

Cometh: A continuous-time discrete-state graph diffusion model

no code implementations10 Jun 2024 Antoine Siraudin, Fragkiskos D. Malliaros, Christopher Morris

Discrete-state denoising diffusion models led to state-of-the-art performance in graph generation, especially in the molecular domain.

Denoising Graph Generation

Aligning Transformers with Weisfeiler-Leman

1 code implementation5 Jun 2024 Luis Müller, Christopher Morris

While recent works aligning graph transformer architectures with the $k$-WL hierarchy have shown promising empirical results, employing transformers for higher orders of $k$ remains challenging due to a prohibitive runtime and memory complexity of self-attention as well as impractical architectural assumptions, such as an infeasible number of attention heads.

Graph Neural Network

Probabilistic Graph Rewiring via Virtual Nodes

no code implementations27 May 2024 Chendi Qian, Andrei Manolache, Christopher Morris, Mathias Niepert

Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm for graph-based machine learning.

Computational Efficiency

Weisfeiler-Leman at the margin: When more expressivity matters

no code implementations12 Feb 2024 Billy J. Franks, Christopher Morris, Ameya Velingker, Floris Geerts

Moreover, we focus on augmenting $1$-WL and MPNNs with subgraph information and employ classical margin theory to investigate the conditions under which an architecture's increased expressivity aligns with improved generalization performance.

Future Directions in the Theory of Graph Machine Learning

no code implementations3 Feb 2024 Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, İsmail İlkan Ceylan, Ron Levie, Derek Lim, Michael Bronstein, Martin Grohe, Stefanie Jegelka

Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences.

Position

Towards Principled Graph Transformers

1 code implementation18 Jan 2024 Luis Müller, Daniel Kusuma, Blai Bonet, Christopher Morris

Empirically, we demonstrate that the Edge Transformer surpasses other theoretically aligned architectures regarding predictive performance while not relying on positional or structural encodings.

Graph Learning

Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems

1 code implementation16 Oct 2023 Chendi Qian, Didier Chételat, Christopher Morris

Recently, machine learning, particularly message-passing graph neural networks (MPNNs), has gained traction in enhancing exact optimization algorithms.

Combinatorial Optimization

Probabilistically Rewired Message-Passing Neural Networks

1 code implementation3 Oct 2023 Chendi Qian, Andrei Manolache, Kareem Ahmed, Zhe Zeng, Guy Van Den Broeck, Mathias Niepert, Christopher Morris

Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input.

Fine-grained Expressivity of Graph Neural Networks

1 code implementation NeurIPS 2023 Jan Böker, Ron Levie, Ningyuan Huang, Soledad Villar, Christopher Morris

In particular, we characterize the expressive power of MPNNs in terms of the tree distance, which is a graph distance based on the concept of fractional isomorphisms, and substructure counts via tree homomorphisms, showing that these concepts have the same expressive power as the $1$-WL and MPNNs on graphons.

Attending to Graph Transformers

1 code implementation8 Feb 2023 Luis Müller, Mikhail Galkin, Christopher Morris, Ladislav Rampášek

Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks.

WL meet VC

1 code implementation26 Jan 2023 Christopher Morris, Floris Geerts, Jan Tönshoff, Martin Grohe

Secondly, when an upper bound on the graphs' order is known, we show a tight connection between the number of graphs distinguishable by the $1\text{-}\mathsf{WL}$ and GNNs' VC dimension.

Weisfeiler and Leman Go Relational

1 code implementation30 Nov 2022 Pablo Barcelo, Mikhail Galkin, Christopher Morris, Miguel Romero Orth

Namely, we investigate the limitations in the expressive power of the well-known Relational GCN and Compositional GCN architectures and shed some light on their practical learning performance.

Knowledge Graphs Logical Reasoning +1

Ordered Subgraph Aggregation Networks

no code implementations22 Jun 2022 Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, Mathias Niepert

Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs.

MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers

1 code implementation27 May 2022 Elias B. Khalil, Christopher Morris, Andrea Lodi

Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems.

Combinatorial Optimization Graph Neural Network

SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks

1 code implementation25 Mar 2022 Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh

While (message-passing) graph neural networks have clear limitations in approximating permutation-equivariant functions over graphs or general relational data, more expressive, higher-order graph neural networks do not scale to large graphs.

Graph Neural Network

Weisfeiler and Leman go Machine Learning: The Story so far

no code implementations18 Dec 2021 Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt

In recent years, algorithms and neural architectures based on the Weisfeiler--Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data.

BIG-bench Machine Learning Representation Learning

Reconstruction for Powerful Graph Representations

no code implementations NeurIPS 2021 Leonardo Cotta, Christopher Morris, Bruno Ribeiro

Empirically, we show how reconstruction can boost GNN's expressive power -- while maintaining its invariance to permutations of the vertices -- by solving seven graph property tasks not solvable by the original GNN.

Graph Reconstruction Graph Representation Learning +1

The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs

no code implementations12 May 2021 Christopher Morris, Matthias Fey, Nils M. Kriege

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for (supervised) machine learning with graphs and relational data.

BIG-bench Machine Learning Node Classification

Deep Graph Matching Consensus

2 code implementations ICLR 2020 Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege

This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.

Ranked #12 on Entity Alignment on DBP15k zh-en (using extra training data)

Entity Alignment Graph Matching +3

Temporal Graph Kernels for Classifying Dissemination Processes

no code implementations14 Oct 2019 Lutz Oettershagen, Nils M. Kriege, Christopher Morris, Petra Mutzel

Hence, we confirm that taking temporal information into account is crucial for the successful classification of dissemination processes.

General Classification Graph Classification

A Survey on Graph Kernels

no code implementations28 Mar 2019 Nils M. Kriege, Fredrik D. Johansson, Christopher Morris

Graph kernels have become an established and widely-used technique for solving classification tasks on graphs.

General Classification Graph Classification

Hierarchical Graph Representation Learning with Differentiable Pooling

14 code implementations NeurIPS 2018 Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

General Classification Graph Classification +4

Global Weisfeiler-Lehman Graph Kernels

1 code implementation7 Mar 2017 Christopher Morris, Kristian Kersting, Petra Mutzel

Specifically, we introduce a novel graph kernel based on the $k$-dimensional Weisfeiler-Lehman algorithm.

General Classification Graph Classification

A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels

no code implementations2 Mar 2017 Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel

On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick.

Faster Kernels for Graphs with Continuous Attributes via Hashing

no code implementations1 Oct 2016 Christopher Morris, Nils M. Kriege, Kristian Kersting, Petra Mutzel

While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well.

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