A study by City College of New York physicists Flaviano Morone and Hernán A. Makse suggests that "smaller is smarter" when it comes to influential superspreaders of information in social networks. This is a major shift from the widely held view that "bigger is better," and could have important consequences for a broad range of social, natural and living networked systems.
In spiking neural networks an action potential could in principle trigger subsequent spikes in the neighbourhood of the initial neuron. A successful spike is that which trigger subsequent spikes giving rise to cascading behaviour within the system. In this study we introduce a metric to assess the success of spikes emitted by integrate-and-fire neurons arranged in complex topologies and whose collective behaviour is undergoing a phase transition that is identified by neuronal avalanches that become clusters of activation whose distribution of sizes can be approximated by a power-law. In numerical simulations we report that scale-free networks with the small-world property is the structure in which neurons possess more successful spikes. As well, we conclude both analytically and in numerical simulations that fully-connected networks are structures in which neurons perform worse. Additionally, we study how the small-world property affects spiking behaviour and its success in scale-free networks.
The success of complex networks at criticality Victor Hernandez-Urbina, Tom L. Underwood, J. Michael Herrmann
We evaluate complex time series of online user communication in Twitter social network. We construct spike trains of each user participating any interaction with any other users in the network. Retweet a message, mention a user in a message, and reply to a message are types of interaction observed in Twitter. By applying the local variation originally established for neuron spike trains, we quantify the temporal behavior of active and passive but popular users separately. We show that the local variation of active users give bursts independent of the activation frequency. On the other hand, the local variation of popular users present irregular random (Poisson) patterns and the resultant temporal patterns are highly influenced by the frequency of the attention, e.g. bursts for less popular users, but randomly distributed temporarily uncorrelated spikes for most popular users. To understand the coincidence in the temporal patterns of two distinct interactions, we propose linear correlations of the local variation of the filtered spikes based on concerned interactions. We conclude that the local variations of the retweet and mention spike trains provide a good agreement only for most popular users, which suggests that the dynamics of mention a user together with that of retweet is a better identity of popular users instead of only paying attention of the dynamics of retweet, a conventional measure of user popularity.
Temporal Pattern of Communication Spike Trains in Twitter: How Often, Who Interacts with Whom? Ceyda Sanlı, Renaud Lambiotte
“We have collected a range of analytical and descriptive understandings of how ideas and practices get scaled”. I see a huge number of innovations passing over my desk nearly every day, and yet, precious few of them spread or scale so that they can have a real impact. Why do we have a open source …
real change in organisations is when you change the way that people connect, and the most profound way in which that connection can be achieved is through small actions that change perceptions in an evolutionary way.
june holley's insight:
"real change in organisations is when you change the way that people connect, and the most profound way in which that connection can be achieved is through small actions that change perceptions in an evolutionary way."
How the Internet is Shaping Social Change, and Social Change is Shaping the Internet Summary As activism for police accountability, fair wages, just immigration, and more takes center stage — social justice movements of the 21st century are using technology to achieve greater scale and reach wider audiences. But are these digital strategies building power Read more
Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform the first systematic methodological study of controversy detection using social-media network structure and content. Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain. Our approach to quantifying controversy is a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, which represents alignment of opinion among users; (ii) partitioning the conversation graph to identify potential sides of controversy; and (iii) measuring the amount of controversy from characteristics of the graph. We perform an extensive comparison of controversy measures, as well as graph building approaches and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.
Quantifying Controversy in Social Media Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis
We here present a model of the dynamics of extremism based on opinion dynamics in order to understand the circumstances which favour its emergence and development in large fractions of the general public. Our model is based on the bounded confidence hypothesis and on the evolution of initially anti-conformist agents to extreme positions. Numerical analyses demonstrate that a few extremists are able to drag a large fraction of conformists agents to their position provided that they express theirs views more often than the conformists. The most influential parameter controlling the outcome of the dynamics is the uncertainty of the conformist agents; the higher their uncertainty, the higher is the influence of anti-conformists. Systematic scans of the parameter space show the existence of two regime transitions, one following the conformists uncertainty parameter and the other one following the anti-conformism strength.
Temporal networks come with a wide variety of heterogeneities, from burstiness of event sequences to correlations between timingsof node and link activations. In this paper, we set to explore the latter by using greedy walks as probes of temporal network structure. Given a temporal network (a sequence of contacts), greedy walks proceed from node to node by always following the first available contact. Because of this, their structure is particularly sensitive to temporal-topological patterns involving repeated contacts between sets of nodes. This becomes evident in their small coverage per step as compared to a temporal reference model -- in empirical temporal networks, greedy walks often get stuck within small sets of nodes because of correlated contact patterns. While this may also happen in static networks that have pronounced community structure, the use of the temporal reference model takes the underlying static network structure out of the equation and indicates that there is a purely temporal reason for the observations. Further analysis of the structure of greedy walks indicates that burst trains, sequences of repeated contacts between node pairs, are the dominant factor. However, there are larger patterns too, as shown with non-backtracking greedy walks. We proceed further to study the entropy rates of greedy walks, and show that the sequences of visited nodes are more structured and predictable in original data as compared to temporally uncorrelated references. Taken together, these results indicate a richness of correlated temporal-topological patterns in temporal networks.
Exploring Temporal Networks with Greedy Walks Jari Saramaki, Petter Holme
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