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Graph Laplacian Mixture Model

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Methods for Clustering Multi-Layer Graphs in Mobile Networks

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

Clustering on graphs has been studied extensively for years due to its numerous applications. However, in contrast to the classic problems, clustering in mobile and online social networks brings new challenges. In these scenarios, it is common that observa ...

2011

Disjoint homometric sets in graphs

János Pach

Two subsets of vertices in a graph are called homometric if the multisets of distances determined by them are the same. Let h(n) denote the largest number h such that any connected graph of n vertices contains two disjoint homometric subsets of size h. It ...

2011

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

On the structure of graphs with low obstacle number

János Pach

The obstacle number of a graph G is the smallest number of polygonal obstacles in the plane with the property that the vertices of G can be represented by distinct points such that two of them see each other if and only if the corresponding vertices are jo ...

Springer Verlag2011

On the Structure of Graphs with Low Obstacle Number

János Pach

Springer Japan2011

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

A lower bound for algebraic connectivity based on the connection-graph-stability method

Martin Hasler, Ali Ajdari Rad, Mahdi Jalili

This paper introduces the connection-graph-stability method and uses it to establish a new lower bound on the algebraic connectivity of graphs (the second smallest eigenvalue of the Laplacian matrix of the graph) that is sharper than the previously publish ...

Elsevier2011

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

Parallelizing Machine Learning- Functionally: A Framework and Abstractions for Parallel Graph Processing

Philipp Haller

Implementing machine learning algorithms for large data, such as the Web graph and social networks, is challenging. Even though much research has focused on making sequential algorithms more scalable, their running times continue to be prohibitively long. ...

2011

Algebraic Algorithms for Vector Network Coding

Christina Fragouli, Javad Ebrahimi Boroojeni

We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying th ...

Institute of Electrical and Electronics Engineers2011