Publications related to Graph database

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Spectral MAB for Unknown Graph Processes

Pascal Frossard, Laura Toni

In this work, we study graph-based multi-arms bandit (MAB) problems aimed at optimizing actions on irregular and high-dimensional graphs. More formally, we consider a decision-maker that takes sequential actions over time and observes the experienced rewar ...

IEEE Computer Society2018

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

A Time-Vertex Signal Processing Framework

Nathanaël Perraudin, Benjamin Ricaud, Andreas Loukas, Francesco Grassi

An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate th ...

Institute of Electrical and Electronics Engineers2018

Distributed Diffusion Adaptation Over Graph Signals

Ali H. Sayed, Jie Chen, Roula Nassif

Most works on graph signal processing assume static graph signals, which is a limitation even in comparison to traditional DSP techniques where signals are modeled as sequences that evolve over time. For broader applicability, it is necessary to develop te ...

IEEE2018

Ramsey-Turan numbers for semi-algebraic graphs

János Pach

A semi-algebraic graph G = (V, E) is a graph where the vertices are points in R-d, and the edge set E is defined by a semi-algebraic relation of constant complexity on V. In this note, we establish the following Ramsey-Turan theorem: for every integer p >= ...

ELECTRONIC JOURNAL OF COMBINATORICS2018

Global and Local Uncertainty Principles for Signals on Graphs

Pierre Vandergheynst, Nathanaël Perraudin, Benjamin Ricaud, David Shuman

Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluating linear signal transforms. Generalizations of such principles to the graph ...

2018

The Sketching Complexity of Graph and Hypergraph Counting

Mikhail Kapralov, John Michael Goddard Kallaugher

Subgraph counting is a fundamental primitive in graph processing, with applications in social network analysis (e.g., estimating the clustering coefficient of a graph), database processing and other areas. The space complexity of subgraph counting has been ...

IEEE COMPUTER SOC2018

A comparison of network embedding approaches

Thành Tâm Nguyên, Chi Thang Duong

Network embedding automatically learns to encode a graph into multi-dimensional vectors. The embedded representation appears to outperform hand-crafted features in many downstream machine learning tasks. There are a plethora of network embedding approaches ...

2018

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Spectral MAB for Unknown Graph Processes

Graph Heat Mixture Model Learning

A Time-Vertex Signal Processing Framework

Distributed Diffusion Adaptation Over Graph Signals

Ramsey-Turan numbers for semi-algebraic graphs

Global and Local Uncertainty Principles for Signals on Graphs

The Sketching Complexity of Graph and Hypergraph Counting

Robust and Efficient Data Clustering with Signal Processing on Graphs

Scalable analysis of linear networked systems via chordal decomposition

A comparison of network embedding approaches

Spectral MAB for Unknown Graph Processes

A Time-Vertex Signal Processing Framework

Graph Heat Mixture Model Learning

Distributed Diffusion Adaptation Over Graph Signals

Ramsey-Turan numbers for semi-algebraic graphs

Robust and Efficient Data Clustering with Signal Processing on Graphs

Global and Local Uncertainty Principles for Signals on Graphs

The Sketching Complexity of Graph and Hypergraph Counting

Scalable analysis of linear networked systems via chordal decomposition

A comparison of network embedding approaches