Publications related to Graph-Based Information Processing

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Geometric deep learning: going beyond Euclidean data

Pierre Vandergheynst, Michael Bronstein

Many signal processing problems involve data whose underlying structure is non-Euclidean, but may be modeled as a manifold or (combinatorial) graph. For instance, in social networks, the characteristics of users can be modeled as signals on the vertices of ...

2017

Stationary signal processing on graphs

Pierre Vandergheynst, Nathanaël Perraudin

Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or network ...

Institute of Electrical and Electronics Engineers2017

Spinner: Scalable Graph Partitioning in the Cloud

Andreas Loukas, Dionysios Logothetis

In this paper, we present a graph partitioning algorithm to partition graphs with trillions of edges. To achieve such scale, our solution leverages the vertex-centric Pregel abstraction provided by Giraph, a system for large-scale graph analytics. We desig ...

Ieee2017

Assembling a Network out of Ambiguous Patches

Matthias Grossglauser, Lyudmila Yartseva, Jefferson Elbert Simões

Many graph mining and network analysis problems rely on the availability of the full network over a set of nodes. But inferring a full network is sometimes non-trivial if the raw data is in the form of many small {\em patches} or subgraphs, of the true net ...

2016

Scaling a Reliable Distributed System

Alexandre David Olivier Maurer

We consider the problem of reliably connecting an arbitrarily large set of computers (nodes) with communication channels. Reliability means here the ability, for any two nodes, to remain connected (i.e., their ability to communicate) with probability at le ...

LPD EPFL2016

Infectious Disease Containment Based on a Wireless Sensor System

Marcel Salathé, Xiao Sun

Infectious diseases pose a serious threat to public health due to its high infectivity and potentially high mortality. One of the most effective ways to protect people from being infected by these diseases is through vaccination. However, due to various re ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Phase Transitions in Group Testing

Volkan Cevher, Jonathan Mark Scarlett

The group testing problem consists of determining a sparse subset of a set of items that are ``defective'' based on a set of possibly noisy tests, and arises in areas such as medical testing, fault detection, communication protocols, pattern matching, and ...

2016

Chaos: Scale-out Graph Processing from Secondary Storage

Willy Zwaenepoel, Laurent Bindschaedler, Amitabha Roy, Jasmina Malicevic

Chaos scales graph processing from secondary storage to multiple machines in a cluster. Earlier systems that process graphs from secondary storage are restricted to a single ma- chine, and therefore limited by the bandwidth and capacity of the storage syst ...

2015

Scale-up Graph Processing in the Cloud: Challenges and Solutions

Willy Zwaenepoel, Amitabha Roy, Jasmina Malicevic

Processing large graphs is an important part of the big-data problem. Recently a number of scale-up systems such as X-Stream, Graphchi and Turbograph have been proposed for processing large graphs using secondary storage on a single machine. The design and ...

Graph-Based Information Processing

Graph Chatbot

Chat with Graph Search

Geometric deep learning: going beyond Euclidean data

Stationary signal processing on graphs

Spinner: Scalable Graph Partitioning in the Cloud

Assembling a Network out of Ambiguous Patches

Graph Signal Processing

Scaling a Reliable Distributed System

Infectious Disease Containment Based on a Wireless Sensor System

Phase Transitions in Group Testing

Chaos: Scale-out Graph Processing from Secondary Storage

Scale-up Graph Processing in the Cloud: Challenges and Solutions

Assembling a Network out of Ambiguous Patches

Stationary signal processing on graphs

Phase Transitions in Group Testing

Geometric deep learning: going beyond Euclidean data

Spinner: Scalable Graph Partitioning in the Cloud

Infectious Disease Containment Based on a Wireless Sensor System

Chaos: Scale-out Graph Processing from Secondary Storage

Graph Signal Processing

Scale-up Graph Processing in the Cloud: Challenges and Solutions

Scaling a Reliable Distributed System