The Shi, Adamic & Strauss paper “Networks of Strong Ties” (pdf, arXiv) analyzes the importance of strong ties in a social network, in contrast to “The Strength of Weak Ties”. If we recall, the strength of weak ties resides in their ability to connect nodes that are not normally in close contact with each other. The benefit is that more novel information can flow between nodes that are not part of one strongly connected component because the nodes in this component are likely to all have similar information due to their close contact. In “Networks of Strong Ties,” the authors argue that while weak ties are useful for spreading new information, obtaining jobs, etc., strong ties play an important role when trust is required in the dissemination of information, and particularly to form the stable foundation of a social network.

Most of this paper was devoted to determining the importance of strong ties empirically. The authors performed simulations using data from two online communities, Club Nexus which comprised a good portion of the student population at Stanford, and the network of AIM links created by the website buddyzoo.com. They did this by altering the data in various ways and analyzing the consequences of these modifications for the network as a whole. They first defined a “threshold 1″ weak tie as a connection between A and B that does not close any triads (implying A and B have no mutual friends) and a strong tie as a connection between C and D that closes at least one triad (implying C and D have a mutual friend). By analyzing the buddyzoo.com network, they found that removing all weak ties had a fairly negligible effect on the size of the giant component, as it dropped from containing 88.9% of the community to having 87.5% of the community. In addition, they analyzed the average shortest path between any two nodes, and this length increased from 7.1 to 7.3 hops after removing the weak ties. I found these small changes surprising because I would have guessed that weak ties greatly shorten average path lengths due to their ability to span strongly connected components. The researchers experimented with tie strength thresholds other than 1, and were able to obtain a smooth relationship between tie threshold, giant component size, and average shortest path (see graph on pg. 4). Their findings led them to conclude that strong ties in this particular network were very robust and that the connectivity of the network could not be compromised by just removing weak ties. Using graph theory, they also proved that the strong ties in a randomly generated graph are not nearly as robust as the strong ties in the social network they studied. This evidence indicates that there are intrinsic qualities in social networks that are measurably human. I found this interesting and wondered about an algorithm that could distinguish a human social network from an artificial one using statistics such as the strength of the strong ties. If this hypothetical algorithm were able to determine the probability that a given large graph represents a real face-to-face network, what would it say about various virtual social networks? Are some types of online communities more similar to real-life social networks than others? What properties of virtual networks differ from real-life networks? Do strong and weak ties play different roles in the real world and the virtual world? I find these questions very interesting, particularly as we find ourselves spending increasingly more time on virtual networks without fully understanding the ways in which they are changing the way we interact with each other.