Complex networks are now being studied in a wide range of disciplines across science and technology. In this paper we propose a method by which one can probe the properties of experimentally obtained network data. Rather than just measuring properties of a network inferred from data, we aim to ask how typical is that network? What properties of the observed network are typical of all such scale free networks, and which are peculiar? To do this we propose a series of methods that can be used to generate statistically likely complex networks which are both similar to the observed data and also consistent with an underlying null-hypothesis -- for example a particular degree distribution. There is a direct analogy between the approach we propose here and the surrogate data methods applied to nonlinear time series data.
This video displays all geolocated tweets related to the #occupygezi #direngezipark protests in Istanbul, from May 31 to June 3, 2013. It shows the high volume of activity on Twitter over this period, and how the protest started in Gezi Park but then spread to the entire city in the matter of hours.
Abstract: This paper proposes a simple social network model of occupational segregation
This paper proposes a simple social network model of occupational segregation, generated by the existence of inbreeding bias among individuals of the same social group. If network referrals are important in getting a job, then expected inbreeding bias in the social structure results in different career choices for individuals from different social groups, which further translates into stable occupational segregation equilibria within the labour market. Our framework can be regarded as complementary to existing discrimination or rational bias theories used to explain persistent observed occupational disparities between various social groups.
Social Network Analysis of The Iliad and The Odyssey Indicates that They Were Likely Based on Real Events “ Today, P J Miranda at the Federal Technological University of Paraná in Brazil .
Odyssey’s social network is small world, highly clustered, slightly hierarchical and resilient to random attacks,” they say. What’s interesting about this conclusion is that these same characteristics all crop up in social networks in the real world.
""We intend to highlight the relational character of various phenomena of pre-modern societies which can be best modelled as complex networks for the purpose of analysis on the basis of case-studies from the late medieval Balkans"
NEW JOURNAL IN 2013Network Science is a new journal for a new discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social,...
A key problem for viral marketers is to determine an initial "seed" set in a network such that if given a property then the entire network adopts the behavior. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds such sets that are several orders of magnitude smaller than the population size
"A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed behave like complex contagions, a few viral memes spread across many communities, like diseases.
We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration"
International School and Conference on Network Science
"Based on the submissions, we collected a book of abstracts, sorted by the main field of application. Within each section, you will find all regular talks (chronologically sorted) and all posters (no special order). Please note that the full book of abstracts is quite large (110 MB), so for a quick look downloading the respective section might be much faster."
How can we identify these pre-revenue startups with significant upside and get involved in them before this happens? I believe there are many publicly available signals that indicate how a company is doing, and I am building tools to track, measure and organize this information.