Real world network datasets often contain a wealth of complex topological information. In the face of these data, researchers often employ methods to extract reduced networks containing the most important structures or pathways, sometimes known as `skeletons' or `backbones'. Numerous such methods have been developed. Yet data are often noisy or incomplete, with unknown numbers of missing or spurious links. Relatively little effort has gone into understanding how salient network extraction methods perform in the face of noisy or incomplete networks. We study this problem by comparing how the salient features extracted by two popular methods change when networks are perturbed, either by deleting nodes or links, or by randomly rewiring links. Our results indicate that simple, global statistics for skeletons can be accurately inferred even for noisy and incomplete network data, but it is crucial to have complete, reliable data to use the exact topologies of skeletons or backbones. These results also help us understand how skeletons respond to damage to the network itself, as in an attack scenario.
Robustness of skeletons and salient features in networks Louis M. Shekhtman, James P. Bagrow, Dirk Brockmann
What do 24,000 ideas look like? Ecologist Eric Berlow and physicist Sean Gourley apply algorithms to the entire archive of TEDx Talks, taking us on a stimulating visual tour to show how ideas connect globally.
The Internet and Social Media change our way of decision-making. We are no longer the independent decision makers we used to be. Instead, we have become networked minds, social decision-makers, more than ever before. This has several fundamental implications. First of all, our economic theories must change, and second, our economic institutions must be adapted to support the social decision-maker, the "homo socialis", rather be tailored to the perfect egoist, known as "homo economicus".
Intense scientific debate is going around the definition of the foundational concepts and appropriate methodological approaches to deal with the understanding of social dynamics. These challenges are aiming to understand human behavior in its complexity driven by intentional (and not necessarily rational) decisions and influenced by a multitude of factors. The functioning of communication-based mechanisms requires individuals to interact in order to acquire information to cope with uncertainty and thus deeply rely on the accuracy and on the completeness of information (if any). In fact, people’s perceptions, knowledge, beliefs and opinions about the world and its evolution, get (in)formed and modulated through the information they can access. Moreover their response is not linear as individuals can react by accepting, refusing, or elaborating (and changing) the received information.
Technology-mediated social collectives are taking an important role in the design of social structures. Yet our understanding of the complex mechanisms governing networks and collective behaviour is still quite shallow. Fundamental concepts like authority, leader-follower dynamics, conflict or collaboration in online networks are still not well defined and investigated – but they are crucial to illuminate the advantages and pitfalls of this form of collective decision-making (which can cancel out individual mistakes, but also make them spiral out of control).
The aim of this satellite is to address the question of ICT mediated social phenomena emerging in multiple scales ranging from the interactions of individuals to the emergence of self-organized global movements. We would like to gather researchers from different disciplines to form a forum to discuss ideas, research questions, recent results, and future challenges in this emerging area of research and public interest.
Particular attention will be devoted to the following topics:
Interdependent social contagion processPeer production and mass collaborationTemporally evolving networks and stream analyticsCognitive aspects of belief formation and revisionOnline communication and information diffusionViral propagation in online social networkCrowd-sourcing: herding behaviour vs. wisdom of crowdsE-democracy and online government-citizen interactionOnline socio-political mobilizationsPublic attention and popularity
All the participants of the satellite meeting (with or without abstract submission) must register for the European Conference on Complex Systems 2013.
Anger spreads faster and more broadly than joy, say computer scientists who have analysed sentiment on the Chinese Twitter-like service Weibo.
One well-known feature of social networks is that similar people tend to attract each other: birds of a feather flock together.
So an interesting question is whether these similarities cause people to behave in the same way online, whether it might lead to flocking or herding behaviour, for example.
Today, we get an interesting insight into this phenomena thanks to the work of Rui Fan and pals at Beihang University in China. These guys have compared the way that tweets labelled with specific emotions influence other people on the network.
And their conclusion is surprising. They say the results clearly show that anger is more influential than other emotions such as joy or sadness, a finding that could have significant implications for our understanding of the way information spreads through social networks.
These guys got their data from Weibo, a Twitter-like service that has become hugely popular in China. In just four years, it has attracted more than 500 million users who post around 100 million messages a day.
Research Paper: http://arxiv.org/abs/1309.2402 (Anger is More Influential Than Joy: Sentiment Correlation in Weibo Rui Fan, Jichang Zhao, Yan Chen, Ke Xu)
Society's techno-social systems are becoming ever faster and more computer-orientated. However, far from simply generating faster versions of existing behaviour, we show that this speed-up can generate a new behavioural regime as humans lose the ability to intervene in real time. Analyzing millisecond-scale data for the world's largest and most powerful techno-social system, the global financial market, we uncover an abrupt transition to a new all-machine phase characterized by large numbers of subsecond extreme events. The proliferation of these subsecond events shows an intriguing correlation with the onset of the system-wide financial collapse in 2008. Our findings are consistent with an emerging ecology of competitive machines featuring ‘crowds’ of predatory algorithms, and highlight the need for a new scientific theory of subsecond financial phenomena.
Abrupt rise of new machine ecology beyond human response time Neil Johnson Guannan Zhao Eric Hunsader Hong Qi Nicholas Johnson Jing Meng Brian Tivnan