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Equivariant Neural Architectures for Representing and Generating Graphs

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Graph Heat Mixture Model Learning

Pascal Frossard, Mireille El Gheche, Hermina Petric Maretic

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all avai ...

IEEE2018

Scalable analysis of linear networked systems via chordal decomposition

Maryam Kamgarpour

This paper introduces a chordal decomposition approach for scalable analysis of linear networked systems, including stability, H 2 and H ∞ performance. Our main strategy is to exploit any sparsity within these analysis problems and use chordal decompositio ...

IEEE2018

Unsupervised Learning of Representations for Lexical Entailment Detection

Andreas Hug

Detecting lexical entailment plays a fundamental role in a variety of natural language processing tasks and is key to language understanding. Unsupervised methods still play an important role due to the lack of coverage of lexical databases in some domains ...

2018

Graph learning under sparsity priors

Pascal Frossard, Hermina Petric Maretic

Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. ...

Ieee2017

Everything You Always Wanted to Know about Multicore Graph Processing but Were Afraid to Ask

Willy Zwaenepoel, Baptiste Joseph Eustache Lepers, Jasmina Malicevic

Graph processing systems are used in a wide variety of fields, ranging from biology to social networks, and a large number of such systems have been described in the recent literature. We perform a systematic comparison of various techniques proposed to sp ...

USENIX Association2017

Graph-based structures in data science

Nathanaël Perraudin

State-of-the-art data analysis tools have to deal with high-dimensional data. Fortunately, the inherent dimensionality of data is often much smaller, as it has an internal structure limiting its degrees of freedom. In most cases, this structure can be appr ...

EPFL2017

Graph-based Methods for Visualization and Clustering

Johann Paratte

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

EPFL2017

Learning time varying graphs

Pascal Frossard, Andreas Loukas, Vasilis Kalofolias

We consider the problem of inferring the hidden structure of high-dimensional dynamic systems from the perspective of graph learning. In particular, we aim at capturing the dynamic relationships between nodes by a sequence of graphs. Our approach is motiva ...

Ieee2017

Geometric Graph Matching Using Monte Carlo Tree Search

Pascal Fua, Jan Kybic, Miguel Amável Dos Santos Pinheiro

We present an efficient matching method for generalized geometric graphs. Such graphs consist of vertices in space connected by curves and can represent many real world structures such as road networks in remote sensing, or vessel networks in medical imagi ...

Ieee Computer Soc2017

Filtering Random Graph Processes Over Random Time-Varying Graphs

Andreas Loukas

Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochasticity i ...

Institute of Electrical and Electronics Engineers2017