Publications related to Representation Learning for Multi-relational Data

Quantization for Distributed Processing and Learning of Structured Data

In the domains of machine learning, data science and signal processing, graph or network data, is becoming increasingly popular. It represents a large portion of the data in computer, transportation systems, energy networks, social, biological, and other s ...

EPFL2022

Graph Embedding for Retrieval

Chi Thang Duong

Information retrieval (IR) systems such as search engines are important for people to find what they need among the tremendous amount of data available in their organization or on the Internet. These IR systems enable users to search in a large data collec ...

EPFL2022

Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets

Pascal Frossard, Chenglin Li, Mingxing Xu

Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph filters with rigid transforms and cannot adapt to signals residing on graphs ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Incompleteness of graph neural networks for points clouds in three dimensions

Michele Ceriotti, Sergey Pozdnyakov

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

Graph integration of structured, semistructured and unstructured data for data journalism

Angelos Christos Anadiotis, Jingmao You

Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists ...

PERGAMON-ELSEVIER SCIENCE LTD2022

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

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

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

A Solution to Adaptive Mobile Manipulator Throwing

Aude Billard, Yang Liu, Aradhana Nayak

Mobile manipulator throwing is a promising method to increase the flexibility and efficiency of dynamic manipulation in factories. Its major challenge is to efficiently plan a feasible throw under a wide set of task specifications. We show that the mobile ...

IEEE2022

Propagation on Multi-relational Graphs for Node Regression

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be conveniently depicted by multi-relational graphs. While inference of continuous node features across a simple graph is rather under-studied by the c ...

2021

Representation Learning for Multi-relational Data

Graph Chatbot

Chat with Graph Search

Quantization for Distributed Processing and Learning of Structured Data

Graph Embedding for Retrieval

Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets

Incompleteness of graph neural networks for points clouds in three dimensions

Graph integration of structured, semistructured and unstructured data for data journalism

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

Annihilation Filter Approach For Estimating Graph Dynamics From Diffusion Processes

Explainability and Graph Learning From Social Interactions

A Solution to Adaptive Mobile Manipulator Throwing

Propagation on Multi-relational Graphs for Node Regression

Graph integration of structured, semistructured and unstructured data for data journalism

Quantization for Distributed Processing and Learning of Structured Data

Graph Embedding for Retrieval

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

Annihilation Filter Approach For Estimating Graph Dynamics From Diffusion Processes

Explainability and Graph Learning From Social Interactions

Incompleteness of graph neural networks for points clouds in three dimensions

Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets

A Solution to Adaptive Mobile Manipulator Throwing

Propagation on Multi-relational Graphs for Node Regression