Publications related to The Power of Two Matrices in Spectral Algorithms for Community Recovery

Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks

Karl Aberer, Thành Tâm Nguyên, Chi Thang Duong

Graph neural networks take node features and graph structure as input to build representations for nodes and graphs. While there are a lot of focus on GNN models, understanding the impact of node features and graph structure to GNN performance has received ...

ELSEVIER2022

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

Nathanaël Perraudin, Andreas Loukas, Karolis Martinkus

We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors. Spectral conditioning allows for direct model ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

On the robustness of the metric dimension of grid graphs to adding a single edge

Patrick Thiran, Gergely Odor, Satvik Mehul Mashkaria

The metric dimension (MD) of a graph is a combinatorial notion capturing the minimum number of landmark nodes needed to distinguish every pair of nodes in the graph based on graph distance. We study how much the MD can increase if we add a single edge to t ...

2022

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Negar Kiyavash, Ehsan Mokhtarian, Saber Salehkaleybar

We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skele ...

2022

Space-Efficient Representations of Graphs

Jakab Tardos

With the increasing prevalence of massive datasets, it becomes important to design algorithmic techniques for dealing with scenarios where the input to be processed does not fit in the memory of a single machine. Many highly successful approaches have emer ...

EPFL2022

A Topological characterisation of Weisfeiler-Leman equivalence classes

Graph Neural Networks (GNNs) are learning models aimed at processing graphs and signals on graphs. The most popular and successful GNNs are based on message passing schemes. Such schemes inherently have limited expressive power when it comes to distinguish ...

2022

The micro-world of cographs

Dominique de Werra

Cographs constitute a small point in the atlas of graph classes. However, by zooming in on this point, we discover a complex world, where many parameters jump from finiteness to infinity. In the present paper, we identify several milestones in the world of ...

ELSEVIER2022

The power of adaptivity in source identification with time queries on the path

Patrick Thiran, Gergely Odor, Victor Cyril L Lecomte

We study the problem of identifying the source of a stochastic diffusion process spreading on a graph based on the arrival times of the diffusion at a few queried nodes. In a graph

G=(V,E)

, an unknown source node

v^* \in V

is drawn uniformly at random, ...

2022

Adaptive majority problems for restricted query graphs and for weighted sets

Abhishek Methuku, Balázs Keszegh

Suppose that the vertices of a graph G are colored with two colors in an unknown way. The color that occurs on more than half of the vertices is called the majority color (if it exists), and any vertex of this color is called a majority vertex. We study th ...

ELSEVIER2021

Sequential metric dimension for random graphs

Patrick Thiran, Gergely Odor

In the localization game on a graph, the goal is to find a fixed but unknown target node v* with the least number of distance queries possible. In the j-th step of the game, the player queries a single node v_j and receives, as an answer to their query, th ...

2021

The Power of Two Matrices in Spectral Algorithms for Community Recovery

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Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

On the robustness of the metric dimension of grid graphs to adding a single edge

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Space-Efficient Representations of Graphs

A Topological characterisation of Weisfeiler-Leman equivalence classes

The micro-world of cographs

The power of adaptivity in source identification with time queries on the path

Adaptive majority problems for restricted query graphs and for weighted sets

Sequential metric dimension for random graphs

Space-Efficient Representations of Graphs

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

The micro-world of cographs

A Topological characterisation of Weisfeiler-Leman equivalence classes

The power of adaptivity in source identification with time queries on the path

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

Sequential metric dimension for random graphs

Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks

Adaptive majority problems for restricted query graphs and for weighted sets

On the robustness of the metric dimension of grid graphs to adding a single edge