Based on a proprietary analysis of over 2000 startup companies in mainly Germany and beyond (predominantly Europe) and their ties to over 500 relevant investors from across the world (angel investors, VC funds across all stages, ranging from the West Coast in the US to Hong Kong) which have invested in those startups, we highlight the importance of social network analysis (SNA). The dynamic network screening, the social capital related insights as well as ongoing portfolio as well as stakeholder monitoring represent just a fraction of possibilities when it comes to social network analysis` relevance for investing in the startup ecosystem. Whether you invest directly, search for co-investors as an investor or even funding as a startup, social network analytic insights will become mainstream in decision making complementing existing financial data in the future.
Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.
“ Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.”
Via Shaolin Tan, Becheru Alexandru
The Bechdel test is a popular tool to analyze the role of women in movies, defining three conditions for a movie to pass the test: It contains two female characters Who talk to each other About something besides a man…
Today I discovered Telenovelas. Telenovelas are short limited run programs similar to soap opera, they are popular in Spanish language counties and they are serious business. I stumbled across a clip on youtube and was instantly hooked. Check this out:…Read more ›
If you want to map cultural hubs throughout time, you can track where history's most notable figures—like Leonardo da Vinci, Jane Austen, and Steve Jobs—were born and died. That was the thinking of Dr. Maximilian Schich, associate professor for art and technology at the University of Texas at Dallas. Schich and his team took data on more than 100,000 notable...
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to $k$-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model's ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.
FIFA World Cup 2014, the biggest sporting event in four years (sorry Olympics) is starting today. The tournament holds 736 players from 32 countries. When the players are not playing for their national teams, they play in 301 different clubs. Players from different national teams meet in these clubs. For example, Manchester United has players from 9 different national teams. This means that players in the World Cup who play in Manchester United know players from at least eight different national teams. Why is this important? If two players belong to the same team (national or club), they have a social connection. Using
In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps group led by Prof. dr. Bart Baesens at the KU Leuven (University of Leuven), Belgium.
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