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Related publications (29)

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Exponential Collapse Of Social Beliefs Over Weakly-Connected Heterogeneous Networks

Ali H. Sayed, Augusto José Rabelo Almeida Santos

We consider a distributed social learning problem where a network of agents is interested in selecting one among a finite number of hypotheses. The data collected by the agents might be heterogeneous, meaning that different sub-networks might observe data ...

IEEE2019

RAQ: Relationship-Aware Graph Querying in Large Networks

Akhil Arora

The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given query grap ...

ASSOC COMPUTING MACHINERY2019

Statistical Physics Methods for Community Detection

Chun Lam Chan

This thesis is devoted to information-theoretic aspects of community detection. The importance of community detection is due to the massive amount of scientific data today that describes relationships between items from a network, e.g., a social network. I ...

EPFL2019

Unsupervised Robust Nonparametric Learning Of Hidden Community Properties

Martin Jaggi, Mikhail Langovoy

We consider learning of fundamental properties of communities in large noisy networks, in the prototypical situation where the nodes or users are split into two classes according to a binary property, e.g., according to their opinions or preferences on a t ...

2019

Generating and identifying functional subnetworks within structural networks

Henry Markram, Kathryn Hess Bellwald, Michael Reimann, Max Christian Nolte, Ran Levi

In one aspect, a method includes generating a functional subgraph of a network from a structural graph of the network. The structural graph comprises a set of vertices and structural connections between the vertices. Generating the functional subgraph incl ...

2018

Graph Signal Processing Of Human Brain Imaging Data

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton

Modern neuroimaging techniques offer disctinct views on brain structure and function. Data acquired using these techniques can be analyzed in terms of its network structure to identify organizing principles at the systems level. Graph representations are f ...

IEEE2018

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

Dynamic Clustering and Control for Large Scale Urban Networks

Mohammadreza Saeedmanesh

Traffic congestion is a significant issue in all urban areas with concentration of activities for various city topologies and distribution of population and land use around the world. Developing realistic models that are able to replicate congestion spread ...

EPFL2018

ChoiceRank: Identifying Preferences from Node Traffic in Networks

Matthias Grossglauser, Lucas Maystre

Understanding how users navigate in a network is of high interest in many applications. We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities. We cast it as a preference lear ...

2017