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Learning Laplacian Matrix in Smooth Graph Signal Representations

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Learning heat diffusion graphs

Pascal Frossard, Daniel Kressner, Xiaowen Dong

Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph. In some applications, this structure may be partly determined by design constraints or pre-determined ...

Ieee-Inst Electrical Electronics Engineers Inc2017

Graph Signal Processing

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature ...

EPFL2016

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

Nathanaël Perraudin, Benjamin Ricaud, Francesco Grassi

Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolv ...

2016

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

Signal-Adapted Tight Frames on Graphs

Dimitri Nestor Alice Van De Ville, Hamid Behjat

The analysis of signals on complex topologies modeled by graphs is a topic of increasing importance. Decompositions play a crucial role in the representation and processing of such information. Here, we propose a new tight frame design that is adapted to a ...

Institute of Electrical and Electronics Engineers2016

Laplacian Matrix Learning for Smooth Graph Signal Representation

Pascal Frossard, Pierre Vandergheynst, 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

Capturing “attrition intensifying” structural traits from didactic interaction sequences of MOOC learners

Pierre Dillenbourg, Patrick Jermann, Nan Li, Tanmay Sinha

This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video click- stream interactions and forum activities, we seek to ...

2014

Tangled Thrackles

János Pach

A tangle is a graph drawn in the plane so that any pair of edges have precisely one point in common, and this point is either an endpoint or a point of tangency. If we allow a third option: the common point may be a proper crossing between the two edges, t ...

Springer Berlin Heidelberg2012

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