We study a two states opinion formation model driven by PageRank node influence and report an extensive numerical study on how PageRank affects collective opinion formations in large-scale empirical directed networks. In our model the opinion of a node can be updated by the sum of its neighbor nodes' opinions weighted by the node influence of the neighbor nodes at each step.
“ The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.”
By studying the network of links between Indian recipes, computer scientists have discovered that the presence of certain spices makes a meal much less likely to contain ingredients with flavors in common.
This new annual computational social science summit is designed to create a broad community of social science researchers - academics, tech industry workers, open data activists, government agency workers, and think tank analysts – dedicated to advancing sociological knowledge through computational methods. Our goal is to foreground social science research and identify areas that can benefit from a deep engagement with computer science and related areas. The Summit will take place over three days, from May 15-17 at Northwestern University’s Kellogg School of Management in Evanston, IL.
The UK’s largest conference for early-career researchers in complexity science is graduating to mainland Europe. The Student Conference on Complexity Science (SCCS) has a focus on computational modelling, simulation and network analysis that extends over the full range of disciplines. The interdisciplinary nature of the conference is attested by its diversity of keynote speakers, delegates, and practical, hands-on workshops.
Since 2010, the SCCS conference series has brought together PhD students and early-career researchers from the UK and beyond, whose interests span areas as diverse as quantum physics, ecological food webs, and the the economics of happiness. This year SCCS will take place for three days in Granada, Spain to engage more international participation.
SCCS 2015 will be held between 9-11 September 2015 in Granada Congress and Exhibition Centre, Spain.
The aim of this conference is to bring together researchers from around the world working on discrete modeling of complex systems and analysis of their dynamics. The objective of this conference is to provide a forum for exchange of ideas, presentation of results of current research and to discuss potential future directions and developments in the field of discrete modeling of complex systems and analysis of their dynamics from methodological and phenomenological point of view. The conference will cover both theoretical and applied research. It will focus on discrete modeling methodologies and their applications to analysis across different scales of dynamics of complex systems. The 2015 Summer Solstice Conference topics include, but are not limited to, the following: • Challenges, benefits and theory of modeling and simulation of complex systems using cellular automata, lattice gas cellular automata, multi-agent based models, complex networks • Discrete models in biology and medicine • Discrete models in economy and social sciences • Discrete models of man made complex systems from nanotechnology to information networks • Tools of analysis of dynamics and multiscale phenomena of discrete models of complex systems There will be sessions of contributed presentations. The organizers reserve the right to assign contributed presentation as oral or poster. The Post Conference Proceedings are planned and all conference presenters will be invited to submit a paper for publication in the Proceedings. All submissions will be peer-reviewed.
There are approximately 3 billion people in the global workforce. LinkedIn's vision is to create economic opportunity for every one of them. The development of the world's first Economic Graph will lead to making that vision a reality. This, of course, is no easy task. Our vision is grand, but it's not unattainable.
So, here's the challenge: Given the wealth of data that exists within LinkedIn, what research would you propose that has the potential to create greater economic opportunity?
We are launching the LinkedIn Economic Graph Challenge to encourage researchers, academics and data-driven thinkers to solve some of the most challenging economic problems of our times.
We develop a framework to track the structure of temporal networks with a signal processing approach. The method is based on the duality between networks and signals using a multidimensional scaling technique. This enables a study of the network structure using frequency patterns of the corresponding signals. An extension is proposed for temporal networks, thereby enabling a tracking of the network structure over time. A method to automatically extract the most significant frequency patterns and their activation coefficients over time is then introduced, using nonnegative matrix factorization of the temporal spectra. The framework, inspired by audio decomposition, allows transforming back these frequency patterns into networks, to highlight the evolution of the underlying structure of the network over time. The effectiveness of the method is first evidenced on a toy example, prior being used to study a temporal network of face-to-face contacts. The extraction of sub-networks highlights significant structures decomposed on time intervals.
Duality between Temporal Networks and Signals: Extraction of the Temporal Network Structures Ronan Hamon, Pierre Borgnat, Patrick Flandrin, Céline Robardet
Friso van Vollenhoven of Xebia uses a combination of Hadoop, Neo4j and browser based visualization and interactive tools to look at graphs, search for known interesting patterns in big graphs and do ad hoc querying against graphs.
Text documents and Excel tables are essentially story-telling devices. They are very useful to communicate information in a logical and chronologically coherent way. However, as the digital networks proliferate, complexity of the stories that need to be told also increases. That’s why it makes sense to embrace networks as the new useful story-telling device
Recent availability of large volumes of ecological and biological data spanning a wide range of scales, made it evident that, despite their diversity, living systems are characterized by patterns/regularities repeated from microscopic to global scales. This workshop aims to explore how these recurrent phenomena emerge as the result of concerted interactions among their constituent elements (e.g. species, or genes), using a combination of approaches inspired by statistical mechanics, network analysis and the theory of complex systems. The aim of the workshop is to introduce students and young researchers with strong background in quantitative sciences to methods and ideas used to describe such complex systems. The range of topics covered at the workshop is very broad, ranging from genomics to ecology, with the ultimate goal of identifying similarities and differences of complex biological systems operating at different scales.
Fee* 150 euro. (*) We will grants 3 fee waivers to support the attendance of PhD students and Junior Post Doctoral researchers (no more than two years from their PhD completion). Prospective participants who are eligible should accompany send an email to the Organizing Committee (LIPh.firstname.lastname@example.org) requiring the fee waiver, providing the CV and a motivation letter. The acceptance will be notified together with the admission to the school.
The aim of the workshop is to bring together applied mathematicians that drive the development of theory for the analysis and control of time-evolving networks with scientists that encounter time-evolving networks in applications like epidemiology and evolution.
“Recognizing the relevance of change: Analysis and control of time-evolving networks in epidemiology and evolutionary medicine”
Ninth IEEE International Conference on Self-Adaptive and Self-Organizing Systems - Cambridge, MA 21 - 25 September 2015
Part of FAS* - Foundation and Applications of Self* Computing Conferences Collocated with: The International Conference on Cloud and Autonomic Computing (CAC 2015) The 15th IEEE Peer-to-Peer Computing Conference
The aim of the Self-Adaptive and Self-Organizing systems conference series (SASO) is to provide a forum for the foundations of a principled approach to engineering systems, networks and services based on self-adaptation and self-organization. The complexity of current and emerging networks, software and services, especially in dealing with dynamics in the environment and problem domain, has led the software engineering, distributed systems and management communities to look for inspiration in diverse fields (e.g., complex systems, control theory, artificial intelligence, sociology, and biology) to find new ways of designing and managing such computing systems. In this endeavor, self-organization and self-adaptation have emerged as two promising interrelated approaches. Many significant research problems exist related to self-adaptive or self-organizing systems. A challenge in self-adaptation is often to identify how to change specific behavior to achieve the desired improvement. Another major challenge is to predict and control the global system behavior resulting from self-organization. Yet more challenges arise from the confluence of self-adaptation with self-organization. For instance, how do self-* mechanisms that work well independently operate in combination? How are meso-level structures formed which leverage micro-level behavior to achieve desirable macro-level outcomes, and avoid undesirable ones?
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