Research shows that being the most connected person is not an effective way to build your network. The single best strategy is one that almost no one talks about.
As you can see in the chart, the further to the right you go toward a closed network, the more you’ll repeatedly hear the same ideas, which reaffirm what you already believe. The further left you go toward an open network, the more you’ll be exposed to new ideas. People to the left are significantly more successful than those to the right.
Public information about one’s coworkers, friends, family, and acquaintances, as well as one’s associations with them, implicitly reveals private information. Social networking Web sites, e–mail, instant messaging, telephone, and VoIP are all technologies steeped in network data — data relating one person to another. Network data shifts the locus of information control away from individuals, as the individual’s traditional and absolute discretion is replaced by that of his social network. Our research demonstrates a method for accurately predicting the sexual orientation of Facebook users by analyzing friendship associations. After analyzing 4,080 Facebook profiles from the MIT network, we determined that the percentage of a given user’s friends who self–identify as gay male is strongly correlated with the sexual orientation of that user, and we developed a logistic regression classifier with strong predictive power. Although we studied Facebook friendship ties, network data is pervasive in the broader context of computer–mediated communication, raising significant privacy issues for communication technologies to which there are no neat solutions.
On the Precision of Social and Information NetworksSocial Resilience in Online Communities: The Autopsy of FriendsterCall Me MayBe: Understanding Nature and Risks of Sharing Mobile Numbers on Online Social NetworksInferring User Interests from Tweet TimesScalable Similarity Estimation in Social Networks: Closeness, Node Labels, and Random Edge LengthsOn the Performance of Percolation Graph Matching
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In many types of network, the relationship between structure and function is of great significance. We are particularly interested in community structures, which arise in a wide variety of domains. We apply a simple oscillator model to networks with community structures and show that waves of regular oscillation are caused by synchronised clusters of nodes. Moreover, we show that such global oscillations may arise as a direct result of network topology.
We also observe that additional modes of oscillation (as detected through frequency analysis) occur in networks with additional levels of topological hierarchy and that such modes may be directly related to network structure. We apply the method in two specific domains (metabolic networks and metropolitan transport) demonstrating the robustness of our results when applied to real world systems.
We conclude that (where the distribution of oscillator frequencies and the interactions between them are known to be unimodal) our observations may be applicable to the detection of underlying community structure in networks, shedding further light on the general relationship between structure and function in complex systems.
If you tweet about your life, a new algorithm can identify your most significant events and assemble them into an accurate life history, say the computer scientists who built it
The problem that Li and Cardie have solved is to find a way of automaticallydistinguishing tweets in the first category from the others. The solution is based on the discovery that that the pattern of tweets, retweets and replies varies for each of the categoroies they’ve defined.
For example, a tweet about starting a new job has a different pattern of responses from followers than a tweet about running or the US election or the weather. So the trick is to identify this ‘Twitter signature’ of these important personal events and then mine the twitter stream for other examples. A chronological list of these events is that person’s life history.
This graduate-level course will examine modern techniques for analyzing and modeling the structure and dynamics of complex networks. The focus will be on statistical algorithms and methods, and both lectures and assignments will emphasize model interpretability and understanding the processes that generate real data. Applications will be drawn from computational biology and computational social science. No biological or social science training is required. (Note: this is not a scientific computing course, but there will be plenty of computing for science.)
Barabasi argues that, rather than being random, humans actually act in predictable patterns. We go along for long periods of quiet routine followed suddenly by loud bursts of activity. Barabasi demonstrates that these breaks in routine, or "bursts," are present in all aspects of our existence— in the way we write emails, spend our money, manage our health, form ideas. Barabasi has even found "burstiness" in our webpage clicking activity and the online news cycle."
This is a guest post by Dr. Ralph Ohr. Dr. Ohr has extensive experience in product/innovation management for international technology-based companies. His particular interest is targeted at the intersection of organizational and human innovation capabilities.
Everyone wishes their content goes viral, like this baby Halloween costume, who got almost 4 million views in two days: But this doesn’t always have to be the case.
What is Perceived Virality?
Perceived virality is rather simple – it’s the sense of content appearing to go viral, but within a targeted or segmented network. Because a lot of people within that specific network are exposed to the content, to them, it appears as “going viral,” even if only a few hundred or few thousand people have seen/shared the content.
How Does Perceived Virality Work?
This is where it gets interesting – perceived virality works because of the overlapping relationships within a network, and the density of these relationships within a network.
One of the main criticisms of Coase’s work is also being tossed out the window. “So, a key criticism is that the [Coase] theorem is almost always inapplicable in economic reality, because real-world transaction costs are rarely low enough to allow for efficient bargaining.” - Wikipedia. In the network era, real-world transaction costs diminish. Furthermore, transaction costs between networked individuals are getting to be less than transaction costs inside organizations. Workers today often have faster access to knowledge outside their enterprises. With knowledge work, this begs the question of why we need organizations for anything other than support.
When a high-profile public figure living in Hong Kong hired the security company Trustwave to test if its experts could get his passwords, they turned to Facebook.
"We found out through Facebook who his wife was," said Jonathan Werrett, a managing consultant for Trustwave's SpiderLabs in Hong Kong. "We found out through her likes -- her public likes -- that she ran a pilates studio. We could then send a phishing email to her based around the fact that she ran a pilates studio that was hiring."
We investigate the behaviour of the recently proposed Quantum PageRank algorithm, in large complex networks.
We investigate the behaviour of the recently proposed Quantum PageRank algorithm, in large complex networks. We find that the algorithm is able to univocally reveal the underlying topology of the network and to identify and order the most relevant nodes. Furthermore, it is capable to clearly highlight the structure of secondary hubs and to resolve the degeneracy in importance of the low lying part of the list of rankings.