Publications de Xiaowen Dong | EPFL Graph Search

Graph Signal Processing for Machine Learning: A Review and New Perspectives

Pascal Frossard, Ntorina Thanou, Xiaowen Dong, Laura Toni, Michael Bronstein

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Learning Graphs From Data: A Signal Representation Perspective

Pascal Frossard, Ntorina Thanou, Xiaowen Dong

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus des ...

2019

Multi-modal Image Retrieval with Random Walk on Multi-layer Graphs

Pascal Frossard, Xiaowen Dong, Renata Khasanova

IEEE2016

Learning Laplacian Matrix in Smooth Graph Signal Representations

Pascal Frossard, Pierre Vandergheynst, Ntorina Thanou, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not alwa ...

Institute of Electrical and Electronics Engineers2016

Laplacian Matrix Learning for Smooth Graph Signal Representation

Pascal Frossard, Pierre Vandergheynst, Ntorina Thanou, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the emerging field of signal processing on graphs. In this paper, we address the problem of learning graph Laplacians, which is similar to learning graph topologies, such that the input data fo ...

2015

Multiscale event detection in social media

Pascal Frossard, Xiaowen Dong

Event detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial resolutions, while in reality events of different scales usually occur simulta ...

Springer2015

Multi-View Signal Processing and Learning on Graphs

Xiaowen Dong

EPFL2014

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

Relationships between entities in datasets are often of multiple nature, like geographical distance, social relationships, or common interests among people in a social network, for example. This information can naturally be modeled by a set of weighted and ...

Institute of Electrical and Electronics Engineers2014

Inference of Mobility Patterns via Spectral Graph Wavelets

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

Modern data processing tasks frequently involve structured data, for example signals defined on the vertex set of a weighted graph. In this paper, we address the problem of inference of mobility patterns from data defined on geographical graphs based on sp ...

2013

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

Relationships between entities in datasets are often of multiple types, which can naturally be modeled by a multi-layer graph; a common vertex set represents the entities and the edges on different layers capture different types of relationships between th ...

2013

Learning of Structured Graph Dictionaries

Pascal Frossard, Xiaowen Dong, Xue Zhang

We propose a method for learning dictionaries towards sparse approximation of signals defined on vertices of arbitrary graphs. Dictionaries are expected to describe effectively the main spatial and spectral components of the signals of interest, so that th ...

2012

Clustering with Multi-Layer Graphs: A Spectral Perspective

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (users) with different edges (pairwise relationships). In this paper, we address ...

Institute of Electrical and Electronics Engineers2012

A Regularization Framework for Mobile Social Network Analysis

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

Mobile phone data provides rich dynamic information on human activities in social network analysis. In this paper, we represent data from two different modalities as a graph and functions defined on the vertex set of the graph. We propose a regularization ...

2011