strong and weak ties

MI 302

Transcript for the Chapter 3 (September 22)

Slide 1: Greetings

Hello everyone! Welcome to Week 4, Chapter 3. It’s been already 4 weeks since this semester began. We have a total of 15 weeks in this semester, so we are a quarter way through. I hope you are getting into the rhythm of this peculiar semester. It is definitely challenging, and in every semester, there are some hiccups in the first couple of weeks. At the 4th week and on, we should settle into our routine. I have set my weekly schedule last week. In the first 1-2 weeks, I try many different timeframes for various tasks of mine, and then estimate the realistic and actual time that it takes to accomplish each task, and then finalize my schedule. Given that we have a very different modality and thus technical challenges in this semester, it took me one more week. But I think I am getting into the rhythm. I hope the same for you.

Slide 2: Recap of the last week

What did we learn last week? We learned the basic concepts of graph theory which allows us to understand a social network at the structural level, not at the behavioral level, which will be explicated by game theory. The graph theory illustrates a network using various terms. Here is the list of essential terms of graph theory—graph, node (a participant, a circle), edge (a link that connects two nodes), path (how the two nodes are connected through links), connectivity (a form of connectivity, which is often described with the “centrality” of a network). The central node in this network? Obviously, the node in the center. Components are a network in which all the nodes are connected, and which stands on its own. Distance means the shortest path that connects two nodes. We talked about small world phenomenon whereby most people in the world are connected by <7 links. We talked about the challenges of depicting networks due to many issues surrounding data collection, such as reluctant and dishonest participants, the complexity of drawing networks, and so on.

We also talked about why connectivity matters or the social implications of connectivity. Empirical data have shown that more than one giant component cannot coexist for a long period of time, and we talked about how Europeans were able to easily dominate the Americas, which can be explained by the clash between two giant components (between Europeans and native Americans) and how one quickly overcame the other. We also talked about how short a distance is between individuals around the globe. This means that your goal obtainment can be more feasible than you actually think.

Slide 3: Today’s learning goals

Today, we will build on these essential elements of a network. The central topic for today is “the strength of weak ties” suggested by Granovetter in 1973. And, from this self-contradicting notion of “strength” of weak times, we will learn about another central topic, social capital, and apply it to what is currently happening in the society. In particular, we will talk about social media influencers who have amassed millions of followers on Instagram and/or Youtube. I’ll present to you a few latest studies about influencers which are based on the “strength” of weak ties and accompanied social capital.

Your textbook author starts Chapter 3 by asking you a thought-provoking question: Why do the majority of people obtain the information about their new jobs from acquaintances, not from their close friends or family members who may have your best interests at heart? Often times, we get to share our entire life stories, or very personal issues, with a stranger who we meet on a plane. This is a very crude example of a weak tie, as you have not met this person before, and will unlikely remain in contact after you land in your destination. Even after 2-3 hours of intense conversations, when the plane lands, you will bid your farewell. How about your connections on social media, such as Linked-In? You may have hundreds of contacts on LinkedIn, many of whom you rarely speak, but you still spy on—meaning you’re lurking on their profiles.

This chapter provides two very important benefits stemming from weak ties: Benefits stemming from the network structure for a weak tie, called bridging property and the other benefits stemming from the properties of “weak ties” called social capital. These are the two main topics of today’s class. If you can fully understand these two topics by the time you finish listening to this video, you can proudly tell yourself that you achieved your goals today. Shall we begin?

Slide 4: Triadic Closure

Let’s define what triadic closure is. If two people in a social network have a friend in common, then there is an increased likelihood that they will become friends themselves at some point in the future.

· This principle is illustrated in Figure 3.1: if nodes B and C have a friend A in common, then the formation of an edge between B and C produces a situation in which all three nodes A, B, and C have edges connecting each other.

· Why does this triadic closure happen?

· Obviously, because both B and C are friends of A, B and C will have more changes to meet with each other.

· Second, because B and C both know A, B and C can trust each other.

· Third, A can actively introduce B to C (or vice versa) because having B and C as mutual friends can provide incentives to A.

· Because of this triadic closure, over time, multiple edges form even though the two endpoints have no neighbors in common as shown the edge between G and D in Figure 3.2(b). (Demonstrate)

· The clustering coefficient of a node A is defined as the probability that two randomly selected friends of A are friends with each other. In other words, it is the fraction of pairs of A’s friends that are connected to each other by edges. For example, the clustering coefficient of node A in Figure 3.2(a) is 1/6 (because there is only the single C-D edge among the six pairs of friends B-C, B-D, B-E, C-D, C-E, and D-E, and it has increased to 1/2 in the second snapshot of the network in Figure 3.2(b) (because there are now the three edges B-C, C-D, and D-E among the same six pairs). (Demonstrate)

· In general, the clustering coefficient of a node ranges from 0 (when none of the node’s friends are friends with each other) to 1 (when all of the node’s friends are friends with each other), and the more strongly triadic closure is operating in the neighborhood of the node, the higher the clustering coefficient will tend to be.

· A higher clustering coefficient means that a person’s friends tend to know one another

Slide 5: Strengths of weak ties

· So how does all this relate to Mark Granovetter’s interview subjects, telling him that their best job leads came from acquaintances rather than close friends? In fact, triadic closure turns out to be one of the crucial ideas needed to unravel what’s going on.

· Let’s start by positing that information about good jobs is something that is relatively scarce; hearing about a promising job opportunity from someone suggests that they have access to a source of useful information that you don’t.

· Now consider this observation in the context of the simple social network drawn in Figure 3.3. The person labeled A has four friends in this picture(C, D, and E), but one of her friendships is different from the others: A’s link to B seems to reach into a different part of the network. (Demonstrate)

· We could speculate, then, that the structural peculiarity of the link to B will translate into differences in the role it plays in A’s everyday life: while the tightly-knit group of nodes A, C, D, and E will all tend to be exposed to similar opinions and similar sources of information, A’s link to B offers her access to things she otherwise wouldn’t necessarily hear about.

· Bridge: We say that an edge joining two nodes A and B in a graph is a bridge if deleting the edge would cause A and B to lie in two different components. In other words, this edge is literally the only route between its endpoints, the nodes A and B.

· Recall what we learned about a giant component in the last chapter: bridges are extremely rare in real social networks. You may have a friend from a very different background, and it may seem that your friendship is the only thing that bridges your world and his, but one expects in reality that there will be other, hard-to-discover, multi-step paths that also span these worlds. In other words, if we were to look at Figure 3.3 as it is embedded in a larger, ambient social network, we would likely see a picture that looks like Figure 3.4.

· In Figure 3.4, Here, the A-B edge isn’t the only path that connects its two endpoints; A and B are also connected by a longer path through F, G, and H. (Demonstrate)

· This kind of structure is arguably much more common than a bridge in real social networks, and we use the following definition to capture it. We say that an edge joining two nodes A and B in a graph is a local bridge if its endpoints A and B have no friends in common — in other words, if deleting the edge would increase the distance between A and B. (Demonstrate)

· And so this is a first network context in which to interpret Granovetter’s observation about job-seeking: we might expect that if a node like A is going to get truly new information, the kind that leads to a new job, it might come unusually often from a friend connected by a local bridge. The closely-knit groups that you belong to, though they are filled with people eager to help, are also filled with people who know roughly the same things that you do.

· Notice that the definition of a local bridge already makes an implicit connection with triadic closure: an edge is a local bridge when it does not form a side of any triangle in the graph.

· (Chime) Doesn’t this contract the triadic closure? According to the triadic closure principle, there should be a link between B and F, as well as A and H, because A is a mutual friend to both B and F, and B is a mutual friend to both A and H. An edge should form between F and B, and A and H. Does it mean that a local bridge violates the notion of triadic closure?

Slide 6: The strong triadic closure property

· In order to understand this notion of a local bridge, which seemingly contradicts the notion of a triadic closure, we need to learn the different levels of strengths in the edges of a social network.

· Each edge of the social network from Figure 3.4 is labeled here in Figure 3.5, as either a strong tie (S) or a weak tie (W), to indicate the strength of the relationship. The labeling in the figure satisfies the Strong Triadic Closure Property at each node: if the node has strong ties to two neighbors, then these neighbors must have at least a weak tie between them. Look at F-J, J-G, and F-G, for an example.

· On the other hand, if the A-F edge were to be a strong tie rather than a weak tie, then nodes A and F would both violate the Strong Triadic Closure Property: Node A would now have strong ties to nodes E and F without there being an E-F edge, and node F would have strong ties to both A and G without there being an A-G edge. (Demonstrate)

· Simultaneously, node H satisfies the Strong Triadic Closure Property: H has a strong tie only to K, but not to others, thus, the absence of a node between G and B does not violate the Strong Triadic Closure Property. (Demonstrate)

· But, conceptually, how do we define the strength of an edge? It is based on the number of phone calls each exchanged, emails exchanged, the frequency by which they talk to each other? The definition of the strong tie depends on the data collection site and your research question.

· An informal, crude definition is that stronger links represent closer friendship and greater frequency of interaction.

Slide 7: Local bridges and weak ties

· Based on this Strong Triadic Closure Property, one can claim that If a node A in a network satisfies the Strong Triadic Closure Property and is involved in at least two strong ties, then any local bridge it is involved in must be a weak tie. Look at Figure 3.6. In this example, the edge A-B is a local bridge. Because the edges A-C and A-B are strong ties, the edge A-B cannot be a local bridge because Strong Triadic Closure says that the B-C edge must exist. But the definition of a local bridge says that the edge B-C cannot exist if the edge A-B were to be a local bridge. To reconcile these two notions, the edge A-B must be a weak tie. If so, the edge A-B can still be a local bridge because it satisfies the Strong Triadic Closure Property.

· In other words, assuming the Strong Triadic Closure Property, the local bridges in a network are necessarily weak ties.

· Because, as I explained earlier, 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.

· Therefore (Drumroll), weak ties, such as the edge A-B in Figure 3.6, connect us to new sources of information and new opportunities. This is the first reason for the “strength” of weak ties. In other words, strong ties by the strong triadic closure property cannot connect two disparate components, and hence are limited in exposing a node to new information and ideas.

Slide 8: The Strength of Network Structure in Large-Scale Data

· Does this property actually hold in the real world?

· Yes, but again a local bridge is VERY rare because of the triadic closure property. Thus, there is the need to generalize or loosen the definition of a local bridge in a large network based on the ratio of the network overlap of an edge between A and B.

· local bridges are the edges of neighborhood overlap 0 — and hence we can think of edges with very small neighborhood overlap as being “almost” local bridges. 1 means that everyone in the neighborhood is connected, and thus there is no local bridge. In the real world, local edges are somewhere between 0 and 1.

Slide 9: Empirical results on tie strength and neighborhood overlaop

· There is one study that shows empirically the capacity of weak-ties connecting and maintaining a social network.

· 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 there is an edge joining two nodes if they made phone calls to each other in both directions over an 18- week observation period.

· 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 and greater strength, and because the curve rises in a strikingly linear fashion, we also get edges of greater and greater neighborhood overlap. The relationship between these quantities thus aligns well with the theoretical prediction: The stronger ties become, the more overlap a neighborhood becomes.

· These graphs on the right correspond to the case in which the links are removed on the basis of their strengths. The black curves correspond to removing first the high-strength links, moving toward the weaker ones, whereas the red curves represent the opposite, starting with the low-strength ties and moving toward the stronger ones. As you can see in A, as strong ties are removed (as shown in the black curve), the entire cellular network shrinks, but the degree of shrinkage is less than that of the red curve. Likewise, the relative size of the largest component (giant component) RGC indicates that the removal of the low links leads to a breakdown of the network, whereas the removal of the high links leads only to the network's gradual shrinkage. (A Inset)

· This study is consistent with a picture in which the weak ties provide the more crucial connective structure for holding together disparate communities, and for keeping the global structure of the giant component intact.

Slide 10: Tie strength, social media, and passive engagement

· Social media increased these weak ties in the society, contributing to the increases in connectivity among disparate people, communities, and societies.

· Burke, Kraut, and Marlow’s 2011Facebook study is a great example showing that how social media contributes to the increases in connectivity in the world.

· This is a common screen capture of a Facebook friend’s list, which will be depicted in the top left-corner graph. The top-right corner graph shows maintained relations, in other words, “following” new posts on News Feed or visiting the friend’s profile more than once.

· The bottom left corner graph shows one-way communication in which users send one or more messages to the friend at the other end of the link

· The bottom right corner graph shows mutual communication in which users both send messages to the friend at the other end of the link and also received messages from them during the observation period.

· As you can see, the connectivity continues to decrease as we move from all friends, maintained relationships, one-way communication, and mutual communication. In fact, a very small fraction of people on the Facebook friends’ lists are in fact communicating with one another. The weak ties here (as defined as “maintained relationships”) therefore sustain the connective structure for holding together disparate communities. Without these maintained relationships, the connectivity will be seriously hampered.

Slide 11: Why does “maintained relationship” (weak ties) matter?

· Let me show you another study on Twitter because Twitter is especially suitable for maintaining weak ties.

· On the x-axis is the total number of friends a user declares, and the curves then show the (smaller) numbers of other link types as a function of this total. There are several interesting conclusions to be drawn from this. First, it confirms that even for users who report very large numbers of friends on their profile pages (on the order of 500), the number with whom they actually communicate is generally between 10 and 20, and the number they follow even passively (e.g. by reading about them) is under 50.

· But beyond this observation, Marlow and his colleagues draw a further conclusion about the power of media like Facebook to enable this kind of passive engagement, in which one keeps up with friends by reading news about them even in the absence of communication. They argue that this passive network occupies an interesting middle ground between the strongest ties maintained by regular communication and the weakest ties from one’s distant past, preserved only in lists on social-networking profile pages. They write, “The stark contrast between reciprocal and passive networks shows the effect of technologies such as News Feed. If these people were required to talk on the phone to each other, we might see something like the reciprocal network, where everyone is connected to a small number of individuals. Moving to an environment where everyone is passively engaged with each other, some event, such as a new baby or engagement can propagate very quickly through this highly connected network.”

Slide 12-14: Bonus slides (updated observations)

· How are these weak ties manifested on social media more recently? How about Influencer Marketing?

· Influencer marketing is a type of micro-celebrity (Senft, 2008) who have accrued a large number of followers on social media and frequently use this social capital to gain access to financial resources (Abidin, 2015). These are some examples of influencer marketing.

· What tools are these influencers using?

· First, they create authentic and creative content to which their audiences can relate.

· Second, they use weak ties! They use their existing ties with their followers to build a bridge between a brand and the brand’s target consumers, who are difficult to reach. Examples of such difficult to reach consumers are high-end consumers, consumers with very unique tastes, and consumers that belong to strong culture subgroups (e.g., immigrants). Influencers play the gatekeeper roles to connect the brand to these target consumers. As a result, brands can effectively increase followers’ engagement with the brand. In my latest publication in this year, we have shown that influencers can increase their followers’ engagement with their own content; over time (over 2 years), their followers eventually start following the brand’s Instagram pages, because the followers were interested in the brands that the influencers wear.

Slide 15: Structural Holes

· What is some other strength of weak ties? Let’s look at 3.11. Node B, with her multiple local bridges, spans a structural hole in the organization — the “empty space” in the network between two sets of nodes that do not otherwise interact closely.

· The argument is that B’s position offers advantages in several dimensions relative to A’s. As mentioned earlier, the first kind of advantage is an informational one: B has early access to information originating in multiple, non-interacting parts of the network. Any one person has a limited amount of energy they can invest in maintaining contacts across the organization, and B is investing her energy efficiently by reaching out to different groups rather than basing all her contacts in the same group.

· A second, related kind of advantage is based on the way in which standing at one end of a local bridge can be an amplifier for creativity. Experience from many domains suggests that innovations often arise from the unexpected synthesis of multiple ideas, each of them on their own perhaps well-known, but well-known in distinct and unrelated bodies of expertise. Thus, B’s position at the interface between three non-interacting groups gives her not only access to the combined information from these groups, but also the opportunity for novel ideas by combining these disparate sources of information in new ways.

· Finally, B’s position in the network provides an opportunity for a kind of social “gate- keeping” — she regulates the access of both C and D to the tightly-knit group she belongs to, and she controls the ways in which her own group learns about information coming from C’s and D’s groups. This provides B with a source of power in the organization.

· For the functioning of the organization, accelerating the flow of information between groups could be beneficial, but this building of bridges would come at the expense of B’s latent power at the boundaries of these groups.

· This is the second strength of weak ties. Weak ties provide social capital to a greater degree than strong ties.

· What is social capital? In general, social capital, for most scholars, includes social network ties and a resource or benefit component from those ties at either the individual actor or collective level, according to Oxford bibliographies. 

· Multiple definitions of social capital exist. James Coleman and others speak of social capital alongside physical capital — the implements and technologies that help perform work — and human capital — the skills and talents that individual people bring to a job or goal. Pierre Bourdieu offers a related but distinct taxonomy, considering social capital in relation to economic capital — consisting of monetary and physical resources — and cultural capital — the accumulated resources of a culture that exist at a level beyond any one individual’s social circle, conveyed through education and other broad social institutions [17, 75].

Slide 16: Bridging as Forms of Social Capital

· Social capital can be a property of a group or a property of an individual. A particular group may have an inclusive culture where everyone is connected or a particular group has especially disparate sub-unites with individuals that are playing gate-keepers roles. Also, one particular individual is good at networking, and thus has personal social capital independent of a group.

· Some of such social capital can be built more easily on strong ties. For instance, if you need intimate relationships, emotional support, and frequent contacts.

· In other cases, social capital can be built easily on weak ties. For instance, if you want to get ahead in the competition, find a relevant job faster than others, become more creative and innovative then, bridging that comes from weak ties can provide substantial benefits.

· What are some of the relatively newer businesses that take advantage of bridging roles of weak ties? How about “sharing economy,” where people no longer own a car, a house, or an equipment, but share others’ properties when they are not in use? Great examples are Uber and AirBnB.

· Let’s watch a video clip from Rachel Botsmann who is a leading thinker of sharing economy. In this Ted-Talk, Rachel Botsmann talks about how weak ties, although she does not use this term directly, facilitate the emergence of a completely new ways of consuming—not buying but sharing.

Slide 17: Closure and Embeddedness

· Thus far, we have talked about the strength of weak ties. Do strong ties also have properties and also benefits? Yes, surely, they do, although they are very different from the properties and benefits of weak ties. One of the advantages of strong ties stems from network embeddedness – the overlap in network neighbors between two users

· Let’s start with node A. Node A’s set of network neighbors has been subject to considerable triadic closure; A has a high clustering coefficient.

· To talk about the structure around A it is useful to introduce an additional definition. We define the embeddedness of an edge in a network to be the number of common neighbors the two endpoints have. Thus, for example, the A-B edge has an embeddedness of two, since A and B have the two common neighbors E and F. This definition relates to two notions from earlier in the chapter.

· First, the embeddedness of an edge is equal to the numerator in the ratio that defines the neighborhood overlap in Equation (3.1) from Section 3.3.

· Second, we observe that local bridges are precisely the edges that have an embeddedness of zero — since they were defined as those edges whose endpoints have no neighbors in common.

· In the example shown in Figure 3.11, what stands out about A is the way in which all of his edges have significant embeddedness. A long line of research in sociology has argued that if two individuals are connected by an embedded edge, then this makes it easier for them to trust one another, and to have confidence in the integrity of the transactions (social, economic, or otherwise) that take place between them. Indeed, the presence of mutual friends puts the interactions between two people “on display” in a social sense, even when they are carried out in private; in the event of misbehavior by one of the two parties to the interaction, there is the potential for social sanctions and reputational consequences from their mutual friends.

· No similar kind of deterring threat exists for edges with zero embeddedness, since there is no one who knows both people involved in the interaction. In this respect, the interactions that B has with C and D are much riskier than the embedded interactions that A experiences. Moreover, B is subject to potentially contradictory norms and expectations from the different groups she associates with. These are the downsides of a weak tie.

Slide 18: Tradeoffs

· Ultimately, then, there are trade-offs in the relative positions of A and B. B’s position at the interface between groups means that her interactions are less embedded within a single group, and less protected by the presence of mutual network neighbors. On the other hand, this riskier position provides her with access to information residing in multiple groups, and the opportunity to both regulate the flow of this information and to synthesize it in new ways.

Slide 19: Key Takeaways

· That’s all from Today’s class. Before we wrap up, I’d like to summarize some of the key takeaways from today’s class.

· We started this class by asking a thought-provoking question: Why do the leads to new jobs come from acquaintances, not from close friends or family members?

· The reason that was provided in Today’s class is summarized as in the “strength” of weak ties.

· What are the strengths of weak ties? First, weak ties connect us to new sources of information and new opportunities. When we discussed this, we talked about a triadic closure property and why a local bridge cannot be a strong tie. Second, those who function as local bridges can control the flow of information and synthesize it in new ways that lead to creativity and innovation. In other words, those have particular social capital that put them at advantage.

Slide 20:

· This week’s discussion question is:

Describe the reasons that new employees have obtained the information about the jobs from acquaintances, not from close contacts. Be sure to describe these reasons based on the notions of triadic closure property and social capital.

Slide 21: Thursday Recitation

· On Thursday, you will do hands-on exercises for triadic closures

· You will do a couple of exercises for strong vs. weak ties

· Lastly, you will continue to work on the export network and apply the property of a triadic closure to the export network.

· Thank you. I I hope you guys have enjoyed today’s lecture, because I have. Have a great week.