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tie strength and network structure in large-scale data in .NET Printer ANSI/AIM Code 39 in .NET tie strength and network structure in large-scale data

tie strength and network structure in large-scale data using barcode creation for visual .net control to generate, create code 3 of 9 image in visual .net applications. GS1 DataBar Overview First, simplifying Code 39 Full ASCII for .NET assumptions are useful when they lead to statements that are robust in practice, making sense as qualitative conclusions that hold in approximate forms even when the assumptions are relaxed. This is the case here: the mathematical argument can be summarized more informally and approximately as saying that, in real life, a local bridge between nodes A and B tends to be a weak tie because, if it weren t, triadic closure would tend to produce short-cuts to A and B that would eliminate its role as a local bridge.

Again, one is tempted to invoke the analogy to freshman physics: even if the assumptions used to derive the perfectly parabolic ight of a ball don t hold exactly in the real world, the conclusions about ight trajectories are a very useful, conceptually tractable approximation to reality. Second, when the underlying assumptions are stated precisely, as they are here, it becomes possible to test them on real-world data. In the past few years researchers have studied the relationship of tie strength and network structure quantitatively across large populations, and they have shown that the conclusions described here in fact hold in an approximate form.

We describe some of this empirical research in the next section. Finally, this analysis provides a concrete framework for thinking about the initially surprising fact that life transitions such as a new jobs are often rooted in contact with distant acquaintances. The argument is that these links are the social ties that connect us to new sources of information and new opportunities, and their conceptual span in the social network (the local bridge property) is directly related to their weakness as social ties.

This dual role as weak connections but also valuable conduits to hard-to-reach parts of the network is the surprising strength of weak ties.. 3.3 Tie Strength and Network Structure in Large-Scale Data The arguments conn .NET 3 of 9 barcode ecting tie strength with the structural properties of the underlying social network make intriguing theoretical predictions about the organization of social networks in real life. For many years after Granovetter s initial work, however, these predictions remained relatively untested on large social networks because of the dif culty in nding data that reliably captured the strengths of edges in large-scale, realistic settings.

This state of affairs began to change rapidly once detailed traces of digital communication became available. Such who-talks-to-whom data exhibits the two ingredients we need for an empirical evaluation of hypotheses about weak ties: the network structure of communication among pairs of people, and the total time that two people spend talking to each other, which can be used as a proxy for the strength of the tie the more time spent communicating during the course of an observation period, the stronger we can declare the tie to be. In one of the more comprehensive studies of this type, Onnela et al.

studied the who-talks-to-whom network maintained by a cell phone provider that covered roughly 20% of a national population [334]. The nodes correspond to cell phone users, and an edge joins two nodes if they made phone calls to each other in both directions over an 18-week observation period. Because the cell phones in this population are generally used for personal communication rather than business purposes, and because the lack of a central directory means that cell phone numbers are generally exchanged among.

strong and weak ties people who already .net vs 2010 bar code 39 know each other, the underlying network can be viewed as a reasonable sampling of the conversations occurring within a social network representing a signi cant fraction of one country s population. Moreover, the data set exhibits many of the broad structural features of large social networks discussed in 2, including a giant component a single connected component containing most (in this case 84%) of the individuals in the network.

Generalizing the Notions of Weak Ties and Local Bridges. The theoretical formulation in the preceding section is based on two de nitions that impose sharp dichotomies on the network: an edge is either a strong tie or a weak tie, and it is either a local bridge or it isn t. For both of these de nitions, it is useful to have versions that exhibit smoother gradations when we go to examine real data on a large scale.

We have just indicated a way to do this for tie strength: we can make the strength of an edge a numerical quantity by de ning it to be the total number of minutes spent on phone calls between the two ends of the edge. It is also useful to sort all the edges by tie strength, so that for a given edge we can ask what percentile it occupies in this ordering of edges sorted by strength. Because a very small fraction of the edges in the cell phone data constitute local bridges, it makes sense to soften this de nition as well, so that we can view certain edges as being almost local bridges.

To do this, we de ne the neighborhood overlap of an edge connecting A and B to be the ratio number of nodes who are neighbors of both A and B , number of nodes who are neighbors of at least one of A or B (3.1). where in the denom inator we do not count A or B themselves (even though A is a neighbor of B and B is a neighbor of A). As an example of how this de nition works, consider the edge A-F in Figure 3.4.

The denominator of the neighborhood overlap for A-F is determined by the nodes B, C, D, E, G, and J, because each of these nodes is a neighbor of at least one of A or F. Of these, only C is a neighbor of both A and F, so the neighborhood overlap is 1/6. The key feature of this de nition is that this ratio in question is zero precisely when the numerator is zero and, hence, when the edge is a local bridge.

So the notion of a local bridge is contained within this de nition local bridges are the edges of neighborhood overlap zero; hence, edges with very small neighborhood overlap can be thought of as being almost local bridges. (Intuitively, edges with very small neighborhood overlap consist of nodes that travel in social circles that have almost no one in common.) For example, this de nition views the A-F edge as much closer to being a local bridge than the A-E edge, which accords with intuition.

Empirical Results on Tie Strength and Neighborhood Overlap. Using these de nitions, we can formulate some fundamental quantitative questions based on Granovetter s theoretical predictions. First, we can ask how the neighborhood overlap of an edge depends on its strength; the strength of weak ties predicts that neighborhood overlap should grow as tie strength grows.

In fact, this dependence is borne out extremely cleanly by the data. Figure 3.7 shows the neighborhood overlap of edges as a function of their percentile in the sorted order of all edges by tie strength.

Thus, as we go to the right on the x-axis, we get edges of greater.
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