Category: Papers

Detecting sequences of system states in temporal networks

Many time-evolving systems in nature, society and technology leave traces of the interactions within them. These interactions form temporal networks that reflect the states of the systems. In this work, we pursue a coarse-grained description of these systems by proposing a method to assign discrete states to the systems and inferring the sequence of such states from the data. Such states could, for example, correspond to a mental state (as inferred from neuroimaging data) or the operational state of an organization (as inferred by interpersonal communication). Our method combines a graph distance measure and hierarchical clustering. Using several empirical data sets of social temporal networks, we show that our method is capable of inferring the system’s states such as distinct activities in a school and a weekday state as opposed to a weekend state. We expect the methods to be equally useful in other settings such as temporally varying protein interactions, ecological interspecific interactions, functional connectivity in the brain and adaptive social networks.

 

Detecting sequences of system states in temporal networks
Naoki Masuda & Petter Holme 
Scientific Reports volume 9, Article number: 795 (2019)

Source: www.nature.com

Percolation and the Effective Structure of Complex Networks

Analytical approaches to model the structure of complex networks can be distinguished into two groups according to whether they consider an intensive (e.g., fixed degree sequence and random otherwise) or an extensive (e.g., adjacency matrix) description of the network structure. While extensive approaches—such as the state-of-the-art message passing approximation—typically yield more accurate predictions, intensive approaches provide crucial insights on the role played by any given structural property in the outcome of dynamical processes. Here we introduce an intensive description that yields almost identical predictions to the ones obtained with the message passing approximation using bond percolation as a benchmark. Our approach distinguishes nodes according to two simple statistics: their degree and their position in the core-periphery organization of the network. Our near-exact predictions highlight how accurately capturing the long-range correlations in network structures allows easy and effective compression of real complex network data.

 

Percolation and the Effective Structure of Complex Networks
Antoine Allard and Laurent Hébert-Dufresne
Phys. Rev. X 9, 011023 – Published 5 February 2019

Source: journals.aps.org

Opinion Mining From Social Media Short Texts: Does Collective Intelligence Beat Deep Learning?

The era of big data has, among others, three characteristics: the huge amounts of data created every day and in every form by everyday people, artificial intelligence tools to mine information from those data and effective algorithms that allow this data mining in real or close to real time. On the other hand, opinion mining in social media is nowadays an important parameter of social media marketing. Digital media giants such as Google and Facebook developed and employed their own tools for that purpose. These tools are based on publicly available software libraries and tools such as Word2Vec (or Doc2Vec) and fasttext, which emphasize topic modeling and extract low-level features using deep learning approaches. So far, researchers have focused their efforts on opinion mining and especially on sentiment analysis of tweets. This trend reflects the availability of the Twitter API that simplifies automatic data (tweet) collection and testing of the proposed algorithms in real situations. However, if we are really interested in realistic opinion mining we should consider mining opinions from social media platforms such as Facebook and Instagram, which are far more popular among everyday people. The basic purpose of this paper is to compare various kinds of low-level features, including those extracted through deep learning, as in fasttext and Doc2Vec, and keywords suggested by the crowd, called crowd lexicon herein, through a crowdsourcing platform. The application target is sentiment analysis of tweets and Facebook comments on commercial products. We also compare several machine learning methods for the creation of sentiment analysis models and conclude that, even in the era of big data, allowing people to annotate (a small portion of) data would allow effective artificial intelligence tools to be developed using the learning by example paradigm.

 

Opinion Mining From Social Media Short Texts: Does Collective Intelligence Beat Deep Learning?

Nicolas Tsapatsoulis and Constantinos Djouvas

Front. Robot. AI, 22 January 2019 | https://doi.org/10.3389/frobt.2018.00138

Source: www.frontiersin.org

Urban sensing as a random search process

We study a new random search process: the \textit{taxi-drive}. The motivation for this process comes from urban sensing, in which sensors are mounted on moving vehicles such as taxis, allowing urban environments to be opportunistically monitored. Inspired by the movements of real taxis, the taxi-drive is composed of both random and regular parts; passengers are brought to randomly chosen locations via deterministic (i.e. shortest paths) routes. We show through a numerical study that this hybrid motion endows the taxi-drive with advantageous spreading properties. In particular, on certain graph topologies it offers reduced cover times compared to persistent random walks.

 

Urban sensing as a random search process
Kevin O’Keeffe, Paolo Santi, Brandon Wang, Carlo Ratti

Source: arxiv.org

Ensembles, Dynamics, and Cell Types: Revisiting the Statistical Mechanics Perspective on Cellular Regulation

•50 years Boolean networks as models for gene regulatory networks

•Random Boolean networks near criticality share properties with genetic networks in cells

•Number of attractors scales as the DNA content raised to the 0.63 power, compares well to current estimate from data (0.88)

•Confirms concept of cell types as attractors and predicts number of cell types

 

Ensembles, Dynamics, and Cell Types: Revisiting the Statistical Mechanics Perspective on Cellular Regulation
Stefan Bornholdt, Stuart Kauffman

Journal of Theoretical Biology

Source: www.sciencedirect.com