Publications related to Equivariant Neural Architectures for Representing and Generating Graphs

Incompleteness of graph neural networks for points clouds in three dimensions

Graph neural networks (GNN) are very popular methods in machine learning and have been applied very successfully to the prediction of the properties of molecules and materials. First-order GNNs are well known to be incomplete, i.e. there exist graphs that ...

IOP Publishing Ltd2022

Deep MinCut: Learning Node Embeddings by Detecting Communities

Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Chi Thang Duong, Trung-Dung Hoang

We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph -structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide insights into the graph structure ...

ELSEVIER SCI LTD2022

Planted matching problems on random hypergraphs

Lenka Zdeborová, Anshul Toshniwal, Gabriele Sicuro

We consider the problem of inferring a matching hidden in a weighted random k-hypergraph. We assume that the hyperedges' weights are random and distributed according to two different densities conditioning on the fact that they belong to the hidden matchin ...

2022

Annihilation Filter Approach For Estimating Graph Dynamics From Diffusion Processes

Pascal Frossard, Arun Venkitaraman

We propose an approach for estimating graph diffusion processes using annihilation filters from a finite set of observations of the diffusion process made at regular intervals. Our approach is based on the key observation that a graph diffusion process can ...

IEEE2022

A Versatile Mapping Approach for Technology Mapping and Graph Optimization

Giovanni De Micheli, Alessandro Tempia Calvino, Heinz Riener, Shubham Rai, Akash Kumar

This paper proposes a versatile mapping approach that has three objectives: i) it can map from one technology-independent graph representation to another; ii) it can map to a cell library; iii) it supports logic rewriting. The method is cut-based, mitigate ...

IEEE2022

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

Constrained Language Models for Interactive Poem Generation

Andrei Popescu-Belis, Gabriel Luthier, Aris Xanthos, Simon Mattei

This paper describes a system for interactive poem generation, which combines neural language models (LMs) for poem generation with explicit constraints that can be set by users on form, topic, emotion, and rhyming scheme. LMs cannot learn such constraints ...

EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA2022

Explainability and Graph Learning From Social Interactions

Ali H. Sayed, Valentina Shumovskaia, Stefan Vlaski, Konstantinos Ntemos

Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of information among the ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Representation Learning for Multi-relational Data

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs.However, research on relational representation learning has so far mostly focused on the ...

EPFL2021

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

Equivariant Neural Architectures for Representing and Generating Graphs

Graph Chatbot

Chat with Graph Search

Incompleteness of graph neural networks for points clouds in three dimensions

Deep MinCut: Learning Node Embeddings by Detecting Communities

Planted matching problems on random hypergraphs

Annihilation Filter Approach For Estimating Graph Dynamics From Diffusion Processes

A Versatile Mapping Approach for Technology Mapping and Graph Optimization

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

Constrained Language Models for Interactive Poem Generation

Explainability and Graph Learning From Social Interactions

Representation Learning for Multi-relational Data

Sequential metric dimension for random graphs

Sequential metric dimension for random graphs

Annihilation Filter Approach For Estimating Graph Dynamics From Diffusion Processes

Representation Learning for Multi-relational Data

A Versatile Mapping Approach for Technology Mapping and Graph Optimization

Incompleteness of graph neural networks for points clouds in three dimensions

Planted matching problems on random hypergraphs

Deep MinCut: Learning Node Embeddings by Detecting Communities

Explainability and Graph Learning From Social Interactions

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

Constrained Language Models for Interactive Poem Generation