Publication

Distributed Graph Learning With Smooth Data Priors

Related publications (156)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Graph-Based Information Processing

Amin Karbasi

We live in a world characterized by massive information transfer and real-time communication. The demand for efficient yet low-complexity algorithms is widespread across different fields, including machine learning, signal processing and communications. Mo ...

EPFL2012

Sequential Group Testing with Graph Constraints

Amin Karbasi, Morteza Zadimoghaddam

In conventional group testing, the goal is to detect a small subset of defecting items

\mathcal{D}

in a large population

\mathcal{N}

by grouping \textit{arbitrary} subset of

\mathcal{N}

into different pools. The result of each group test $\mathcal{T} ...

Ieee2012

Privacy and Dynamics of Social Networks (PhD Thesis: pre-print)

Pedram Pedarsani

Over the past decade, investigations in different fields have focused on studying and understanding real networks, ranging from biological to social to technological. These networks, called complex networks, exhibit common topological features, such as a h ...

2012

Robust Non-Rigid Registration of 2D and 3D Graphs

Pascal Fua, Jan Kybic, Przemyslaw Rafal Glowacki

We present a new approach to matching graphs embedded in R2 or R3. Unlike earlier methods, our approach does not rely on the similarity of local appearance features, does not require an initial alignment, can handle partial matches, and can cope with non-l ...

2012

Beyond Graphs: A New Synthesis

Dario Floreano, Claudio Mattiussi, Daniel Marbach, Peter Dürr

Artiﬁcial neural networks, electronic circuits, and gene networks are some examples of systems that can be modeled as networks, that is, as collections of interconnected nodes. In this paper we introduce the concept of terminal graph (t-graph for short), w ...

2011

Space crossing numbers

We define the crossing number for an embedding of a graph G into R^3, and prove a lower bound on it which almost implies the classical crossing lemma. We also give sharp bounds on the space crossing numbers of pseudo-random graphs. ...

2011

Content Preference Estimation In Online Social Networks: Message Passing Versus Sparse Reconstruction On Graphs

We design two different strategies for computing the unknown content preferences in an online social network based on a small set of nodes in the corresponding social graph for which this information is available ahead of time. The techniques take advantag ...

2011

Semi-Supervised Learning with Spectral Graph Wavelets

Pierre Vandergheynst, David Shuman, Mohammadjavad Faraji

We consider the transductive learning problem when the labels belong to a continuous space. Through the use of spectral graph wavelets, we explore the benefits of multiresolution analysis on a graph constructed from the labeled and unlabeled data. The spec ...

2011

Leveraging User-Generated Content for Information Discovery on the Web

Adriana Budura

The large-scale adoption of the Web 2.0 paradigm has revolutionized the way we interact with the Web today. End-users, so far mainly passive consumers of information are now becoming active information producers, creating, uploading, and commenting on all ...

EPFL2010

Towards unifying diffusion and exemplar-based inpainting

Pierre Vandergheynst, Emmanuel D'Angelo

A novel framework for image inpainting is proposed, relying on graph-based diffusion processes. Depending on the construction of the graph, both flow-based and exemplar-based inpainting methods can be implemented by the same equations, hence providing a un ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Sequential Group Testing with Graph Constraints

Amin Karbasi, Morteza Zadimoghaddam

In conventional group testing, the goal is to detect a small subset of defecting items

\mathcal{D}

in a large population

\mathcal{N}

by grouping \textit{arbitrary} subset of

\mathcal{N}

into different pools. The result of each group test $\mathcal{T} ...

Ieee2012