**Transcript of Cyberseminar**

**Enhancing Implementation Science**

**Advanced Program Session 4- Social Network Approaches for Behavior Change and Program Implementation**

**Presenter: Thomas Valente, PhD**

**November 15, 2012**

**Advanced Program Session 4- Social Network Approaches for Behavior Change and Program Implementation**

**November 15, 2012**

Speaker:I would like to introduce today’s presenter. Today’s presenter is Dr. Thomas Valente, Professor at the Institute for Prevention Research with the Keck School of Medicine, the University of Southern California. Tom, can I turn things over to you? 

Tom Valente:Yes, you certainly can. Thank you, Heidi, for the introduction, thank you, Brian, for the invitation to participate in the series, and thank you all for tuning in to find out what I have to say about social network analysis. I will go ahead and start and hopefully everybody can see my screen. 

Just a quick acknowledgement, most of this work has been funded by the National Institutes of Health, in particular NIAA, the National Institute on Alcohol and Alcohol Abuse; and the National Cancer Institute as well as the National Institute on Drug Abuse. I am very grateful to the NIH for its support. 

My presentation this morning I have broken up into four different sections. I am going to initially discuss models of diffusion, innovations and behavior change; that is the theoretical orientation that I have been working from in order to develop these ideas. Then, I will turn my attention specifically to network models and diffusion. How social networks influence the diffusion and adoption process, and then I will talk a little bit about social influence. Why networks are so important for understanding how people are influenced by their peers; and then I want to devote considerable time to research our network interventions, what we know about how social networks can be used to accelerate behavior change and what our experiences to date are with that process. 

I wanted to start the presentation with a poll question asking how much experience people have with social network analysis. I have broken the responses up into five different categories. Hopefully you all will be able to answer that question and provide a little bit of experience for me just so I understand how much you people know in general about this topic. 

Network analysis can be a fairly complicated subject. It is deceiving because it is intuitive and we all have networks, and we all know a lot about networks. Yet, on the other hand, the models that we use and have developed over the past few decades can be quite complex involving mathematics, involving specialty software, and sometimes involving very specific types of analyses. I like to emphasize to people that network analysis can be anything that you want it to be in some ways; it can be very simple providing basic information; or it can be quite complicated, providing very in depth information about the subject of the application area that you’re working in. 

Speaker: Tom, here are your results. 

Tom Valente:Oh, terrific, okay thank you. We basically have an audience that’s mostly pretty unaware of social network analysis. In fact, it looks like seventy-three percent of the audience is in the minimum or some category, so I will try not to be too technical in my presentation. 

The orientation is to understand how change happens and in my world, I think about change happening as one in which new ideas and practices enter a community or group from some external source. This could be a person moving from one area to another, the so-called cosmopolitan contact. It could be media communications of all different varieties. It could be technical changes, shifts in the underlying economy. Then what is critical for my analysis is that we have substantial evidence that that change then spreads through interpersonal contact. Interpersonal contact is the key mechanism by which people are persuaded to understand and adopt new ideas. 

The diffusion perspective has been around for a long time. The theory originally was starting to be developed in the early 1900s, and it was a landmark publication in 1943 by Brice Ryan and Neil Gross that documented how new ideas and practices spread. In particular, they were studying the spread of hybrid seed corn. There’s a long story to be told about hybrid seed corn, how it was developed in agricultural extension laboratories and then spread to farmers and used by farmers initially in the midwest and eventually throughout the entire United States and eventually the entire globe. All of the corn that has grown today is of hybrid varieties. It is done with hybrid seeds. 

Brice Ryan and Neil Gross plotted the rate of new adoption and cumulative adoption of these hybrids with their analysis by asking farmers when they first started to use hybrid seeds. This was a new innovation, it was radically different from what had happened before because for the first time farmers would be forced to buy seed rather than using their own. Ryan and Gross found this very nice S-shaped curve, which has been replicated in a lot of areas. Initially there were very few adopters, and then the rate of adoption starts to accelerate dramatically until we get up to the top where it plateaus. There are no more new adopters left in the community and everyone has adopted. 

Brice Ryan and Neil Gross did not advocate using mathematical models to map on to these curves, although a lot of people, particularly economists, have done so in the past couple of decades. This “S” shaped curve, not surprising, any growth system, any growth in a technology or an idea will follow this kind of curve and what’s actually interesting is deviations from this curve. 

So how does this process work? Well, we have something called the homogenous or random mixing model and it is one you can do in your own home on your own computer in an Excel spreadsheet. You can start out with a population of a hundred non-adopters, this is some random community of a hundred people and you can specify that five of those one hundred, were initial adopters. These are the people that I said earlier what might initially adopt through cosmopolitan contact, media communications and so on. We know something about those innovators, who those first people are. That is not primarily what we are interested in. Once those five people have adopted, they interact with the ninety-five non-adopters left in the system and convert them to adopt at a rate of one percent, and then you get four point seven-five new adopters. 

You can also think of this as a disease. Five people have a [inaudible] and ninety-five people do not, but are susceptible. They come into contact and spread the disease at a rate of one percent and you get four point seven-five that are now newly infected. Now you have a pool of nine point seven-five infected interacting with the non and the conversion rate happens, and so on through the process. It turns out, if you start with a hundred people at a rate of one percent over a time period, you get his nice S-shaped curve, normal logistic growth curve and you can plot the number of newly infected, or new adopters over time, which sort of approximates a bell shaped curve. That is the standard diffusion. That is the way things spread in nature and in society and so on. However, we know from a lot of research, these are data going back to... that were collected in the 1950s, and here is the standard S-shaped curve that you might get in this community. And in this study, we have information on when doctors adopted a new drug that was available at the time, tetracycline. We also collected information, not me, James Coleman, Elliott Katz, and Herbert Mendel back in the 1950s at Columbia University. They collected data on which of the other physicians that these doctors went to for advice and discussion; and discovered deviations from this S-shaped curve based on your network position. That is, people with no connections were much slower to adopt and they had a fairly slow diffusion curve. We call this more of a log... lag type diffusion. Whereas those that were highly connected, three or more connections, diffusion accelerated amongst that group and it spread much more rapidly. Coleman, Katz, and Mendel concluded from these data that perhaps it was evidence for a contagion effect with the snowballing of the diffusion process. 

I analyzed those Coleman, Katz, and Mendel data for my dissertations to try to develop new network models of diffusion and the minute I graduated with my PhD in 1991, I was immediately interested in trying to replicate the Coleman, Katz, and Mendel study. I am pleased to say that after only twenty years we had the opportunity to do so. I worked with colleagues at the Wharton School of Business [inaudible] and some colleagues from the private sector in pharmaceutical marketing at [inaudible] and we had a client who was interested in collecting network data among physicians and sharing with us in exchange for us giving them the network data, they would share with us the prescribing data from a product launch out three years. 

Lo and behold, we mapped those networks and one of the things that we found is that these physicians were indeed connected to one another through discussion networks; and very interestingly, we had a very strong network signature structure. In other words, we found that the network was characterized by homophile, which is not surprising. We find that in most networks; and by homophile, I mean people are connected to one another if they have similar traits and attributes. You can see from these data that those physicians with European surnames were much more likely connected to one another and those with Asian surnames were connected to one another over here. We also found a few physicians that were very highly connected. Many people reported going to them for discussion about clinical domain; but we also see this is a very typical sort of network signature in that these three physicians and to a lesser extent this fourth one, are all structurally equivalent. In other words, they are connected to the same other people. So from a diffusion perspective, they are quite redundant and from a marketing perspective, spending your time sending detail agents to each one of these four people is not a good use of resources because they are all going to influence the same levels. 

On the other hand, there is a physician over here who is quite influential with an entirely different group, and this physician was being ignored by the marketing team of this client, and when they saw this data, they were quick to recalibrate their marketing efforts. Upon doing so, we then had data on how quickly diffusion occurred for this new product over time and one of the things that you see is those central physicians, the ones that are central to the network were earlier adopters of the new drug and it emanated and spread from them out to other physicians over time. So, the client was quite happy with their ability to recalibrate their efforts and I think the physicians were happy to be part of the diffusion process. 

One other side note that we investigated in this study, is we were interested in trying to determine how correlated opinion leadership is when it is defined by colleagues, that is sociometric opinion leadership, other people may use an opinion leader, versus self-reported opinion leadership. There is a validated opinion leadership scale in the literature. We looked at the correlation between these two measures; we find it to be point four-three. That is consistent with other estimates and it indicates that people who think they are opinion leaders are not necessarily viewed as such by their colleagues and by their peers. We also discovered an interesting finding, and that is the self-reporting opinion leaders were less likely to be influenced by their colleagues to adopt this new drug, whereas those that were sociometric opinion leaders were no more or less susceptible. 

We can also study diffusion at a more global level, that is policy diffusion. We are now engaged in some research trying to understand factors at the country level that would be associated with country ratification of a new World Health Organization treaty called the Framework Convention for Tobacco Control. This was passed by the WHO in 2003 and over the past approximately ten years has been ratified by eighty-five percent of the countries. The U.S. has not ratified the FCTC, nor is it likely to; but you can see some sort of examplar countries where they are on the diffusion curve over time. 

We also happen to have data about interactions and communications among tobacco control advocates on something called Globalink; and we can map the network. One of the things that we have found is that the earliest adopting countries were quite well connected through Globalink whereas the non-ratifying countries were not a part of Globalink and not connected to each other. In fact, there were only three countries that have any significant amount of connectivity in Globalink of the non-ratifiers. Those three are the United States, Switzerland, and Argentina. Then, not surprisingly, the U.S.A acts quite different in the international stage and Switzerland is highly connected here because, of course, the WHO is located in Geneva, which is in Switzerland. The Swiss are also somewhat reluctant to ratify international treaties. So, in short, there is a network diffusion process that seems to occur for individual behavior, we have some examples from physicians; and also seems to occur when we talk about things like countries and states and other non-human types of nodes, or actors. 

Just as an aside, there also are a couple of other World Health Organization treaties that have been ratified by many countries over this same time period and in this research we will be comparing the influences between countries and across different networks. 

Now I want to shift my attention a little bit from the general view of diffusion through networks and sort of drill down a little bit to the micro level and try to understand social influence at the interpersonal level. How do networks influence individual people to change their behavior? A typical model is this network exposure model. We know from a lot of research that as your friends and colleagues start to do something, you are more likely to do it. So, we have done research in areas that, like tobacco, we know that if your friends smoke, you are more than two times as likely to smoke yourself if you’re an adolescent. If your friends drink, you are about two times as likely to start drinking. We know that bullying and victimization is influenced by these networks. If you are in a network with victims or bullies, you are more likely to do the same, and we know, as I said, with the physician data from a couple different studies that as your colleagues start to do something, you are more likely to do it yourself. Now, this is a simple model, it is very straightforward and of course, as academics and scientists, we can make this considerably more complicated. We can influence... we can weigh these network influences not only by direct ties, but by indirect ties. Some people may be influenced by those that they are two steps away from, or three steps, or four steps away from; and the open research question is whether or not these indirect influences need to be weighted somehow as being less important or as important; and do these indirect influences depend on the behavioral dispositions of the intermediate individuals. We really have not tested those various models to figure that out. 

We can also weight interpersonal influences by something called structural equivalence. From a network perspective, structural equivalence is the degree that two individuals occupy the same position in the network. In this network here, persons A and B are connected to the same others and have the same relations to the same others. They are perfectly structurally equivalent and you will notice they don’t have to be connected to one another to be structurally equivalent and we have had instances where sometimes diffusion occurs via structural equivalence, and this is particularly true in the case of industries and firms. Firms monitor the behavior of other firms that are in the same position of the market as they are, and are oftentimes influenced by their behaviors. 

Of course, we know that tie strength matters. In other words, people are most likely to be influenced by those they’re strongly connected to than those they’re weakly connected to, so for example, we know that people are much more strongly influenced by their spouses, say, than by their friends or casual acquaintances. We know that risk behavior is much stronger among these strong, closer ties than among weaker ties, and so on. That intuitively makes sense and the empirical evidence seems to fall in line with that. 

We also know that an individual is influenced by their peers in part, dependent on how those peers are connected to each other. This person, number two here, is in an interpersonal environment where their friends are connected to one another. The behavior of those friends is more strongly associated with ego’s behavior than in a situation like this, number 5, for whom many of their contacts are not connected to each other, but instead span out into the network giving access to novel information, behavioral influences and resources.  

Finally, another factor that we have discovered in this interpersonal influence process is the existence of thresholds. By thresholds, I mean people who are willing to engage in behaviors when few of their [inaudible] have already engaged in it, so for example, this person here is willing to adopt when only one of their friends has adopted, whereas this person here waits until two of their friends adopt before they are willing to do so. So it turns out that people are oftentimes vary quite a bit in their personal network thresholds. Some people have very low thresholds and like to do things before their colleagues or peers, other people have high thresholds, very resistant to change, wait until everybody does it before they are willing to do it. I often confess that I am a very high threshold adopter. I wait until everybody else is doing something before I’m willing to do it, that way I can wait until the cost goes down and I can be assured that I have people in my network that I can turn to for information support and so on when I’m making my adoption decisions. 

Then here is a graph of social network thresholds from one classic data set on women’s adoption of family planning in Korean villages. One of the things that we now know is that if there are people who are prominent in the network, receive lots of ties... say in answer to a question, who do you go to for advice, and they have low thresholds, that can accelerate the diffusion process quite considerably. 

In sum, there are many different kinds of weights that can influence the social influence equation. In other words, we often times think of networks as being a very simple process. When our friends do something, we are more likely to do it. However, it turns out that there are many other contingencies that one can impose on the situation to sort of complicate the role of that interpersonal influence. I think that is also consistent with our intuition. Yes, I do things when my friends do them, but really, it depends on which friends exactly, which kinds of behaviors and under which circumstances I am willing to make that adoption decision. I will say we know that social networks are very important influences on behaviors. This is a graph that just... if you don’t understand it, don’t worry, it takes about a half hour to explain, and I don’t have time to do that; but it is introducing the notion that some of these network influences in fact vary over time. They are dependent on where you are in the stage of diffusion and vary in our estimate depending on whether you are early in the process or later in the process. We are now engaged in building some statistical analytic platforms that will enable us to test this process out in the context where we have evolving networks and behavior change at the same time. 

This is just a slide on that dynamic estimation and from a scientific point of view, it’s important because it will help us further articulate how these network influences can be estimated in a dynamic situation where we have network evolution and behavior change at the same time. 

If networks are so important, and we certainly think that they are quite important, it raises the question of whether or not we can use networks to accelerate behavior change. Of course, this is... I’ve been doing network analysis research for about twenty-five years and very early on in my career, when I would visit private clients or even other academics, one of the questions would be, okay it’s great, networks are important, you may or may not convince me of that, but if so, how can I use it to make things better? How can I use it to improve organizational performance? How can I test theoretically interesting aspects of social network theory and, actually social science theory, by using network analysis interventions in laboratory or real world experimental settings? 

For some time, I have been interested in this and have been developing models for its application and conducting some various field studies. So, what are network interventions? Well, not surprisingly if I take a very, let’s say catholic, small “c,” open view to what constitutes a network intervention that is any time that we are using network data to change behaviors. These can be individual behaviors, or even things occurring at the community or organizational level. Any time we use the network data to identify change agents or champions, to find sub groups that we want to deliver our intervention to, or any intervention which we are trying to change network structure. Many behavioral change programs are specifically designed to alter the network structure, putting people who do not have resources or social supports in contact with those who do, and so on. Even if it’s just an assist... even if it’s just program monitoring or process monitoring of network data to find out how the program is being implemented, that would constitute a network intervention. 

Before I describe network interventions, there are three sort of caveats or principles that I think are very important to underscore and to make sure were highlighted before we begin our work. That first principle is that the program goals matter quite a bit when we are selecting a network intervention. In other words, in some settings we are trying to increase network cohesion and in others we may be trying to decrease cohesion and increase fragmentation. For example, I may have as a goal that I want to slow the spread of STDs in the community. Well, I can do that in two different ways. We can accelerate and try to increase the uptake of condom use, or I can try to make the sexual contact network more fragmented by pulling people out of the network who are, say high transmitters or part of the poor transmission network. I like to say that network interventions are not agnostic to content and what when I was a young man, I always had a dream that I would write a computer program which people just pressed a button and away it goes and takes care of all the work for them and magically transforms their organization from being inefficient to efficient. Turns out that is not true; it is never going to be true and that you have to understand at a deep level, what is going on in your community and what kinds of behavior changes you are trying to bring about with your community. Network analysis is not agnostic to content, what you’re trying to do, and in fact I oftentimes think of network analysis as being a diagnostic tool which you can use to get a deep understanding of your organization or your community and I have oftentimes referred to this as mathematical ethnography. 

The second principle for network interventions is that having a good well-developed theory of the behavior that you are trying to change is very important. Having network analysis as a tool doesn’t release us from the obligation to understand the motivations for behavior change, the barriers against changing behavior, what are the cultural issues that may be occurring, what are the technical issues that may be in place that inhibit, or facilitate, behavior change. We need to have those issues well articulated and documented. I am a big proponent of developing good theory-based interventions and that is certainly the case in the network context. In fact, some people argue that it is more important in the network context because we have a lot more information about how the community is structured. 

The third principle is one that ironically I learned from the pharmaceutical companies, ones that we oftentimes think are just there to try to make money, they have always been... at least the people I have worked with... very clear that it is important to learn from any community that you’re trying to change. You want to learn from them what their needs are, what their perspectives are, all the things I’ve talked about so far. It is critical that you understand and use the network analysis as a tool to elicit information from your community, treat it as a dialogue, not as an asymmetric push notion, but instead one that’s a dialogue between you, the programmers, and the community you’re trying to change. You will notice, actually, that I never use the word target. I think that is an inappropriate word for us to use for a behavior change program. I oftentimes tend to think that we work with audiences; we elicit from them their perspectives and ideas and engage in a dialogue with them, rather than just trying to impose our will on them. 

Those principles being stated can be very clear, let’s discuss what network interventions are, and look like. Well, just this past summer I published an article in Science, in which I lay out four different broad strategies that exist for network interventions. Each one of these strategies is a broad category that involves a certain set of different tools and each strategy has a number of tactics that can be employed or used in the development of the network intervention. Amazingly, each one of these tactics oftentimes has multiple specific mathematical algorithms for operationalizations for them. The quick way to articulate one example, the top line of this table, to illustrate what I mean by strategy tactic and operationalizations. 

The broad strategy is identification of individuals to be champions; your first adopters are the people you want to be disseminating your message. While we most oftentimes think of doing that in the context of identifying leaders within organizations or communities to act as champions, the most common way that we have done that is to simply count the number of times a person is nominated as a leader or nominated as someone whom people go to for advice. The people highest in degree are the leaders who may become our change agents. However, in the social network analysis community, we have nearly a dozen different specific mathematical algorithms that define what it means to be at the center of a network. You can be the center of a network if you receive a lot of nominations. You can also be at the center of a network if you are closer to everybody else in the network on average, so fewer steps separate you. Or, you can be in the center of a network if you have high [inaudible]. That is, you lie on the shortest path connecting others in the network. Or we can use any mathematical procedures to also characterize high centrality nodes, people who have high values on the first [inaudible]. We can use power, which is the, you are central to the extent that you are connected to central other people, and so on and so forth. 

While we have a very simple definition in operationalization, we also have many other choices that we can use that may or may not be appropriate depending on our setting and other factors in the intervention. What that tells you, and what is important about this table is we have four broad strategies; each strategy has several tactics. In fact, there are probably about twenty different tactics here in this column, and then each tactic may have separate operationalizations. This, as a conservative estimate, provides something on the order of forty to fifty different choices that you have for picking what kind of network intervention you may want to do. I think that is very important to acknowledge because we treated this as a fairly simple exercise and I always encourage people to do so. However, it can get quite complicated if you want it to be. 

Now, in the remaining part of this presentation, I’m going to walk through just a couple examples from this table where we have done studies, we have data, we know if it works or it doesn’t, and then I’ll return to some thoughts about how you select the appropriate network intervention for your setting. 

The most straightforward type of network intervention is to identify change agents using network data. You collect the network data, you know what kind of people you want to recruit to be your champion, then you train them and have them go out and be your ambassadors. Now, leaders is the most common technique. There is also a similar idea that was developed by Steve Burgotti called key players. Key players are leaders, but also by identifying key players, we avoid the overlap problem I discussed in the example of the physicians from San Francisco where we saw that many of those leaders were in fact structurally prevalent and connected to the same others. 

We have not done this, but you might think that in many settings, finding those individuals who bridge various sub groups in the network would be the most important ones to start with. Bridges connect people that are not... connect groups that are otherwise unconnected or sparsely connected, and those bridges can be very important in building a more cohesive organization. We have seen in many settings where there is a new merger that takes place between divisions or departments that oftentimes there are very few bridges that hold that merger together and sometimes it’s important to strengthen those bridges or even expand them and build more to make for a better, more cohesive organization. 

In some cases you may want to identify those people who are marginal or peripheral in the network. We know that those students who are marginal in friendship networks and disconnected from friendship and adult networks are more at risk for suicide, suicide ideation and suicide attempts, so you may want to design interventions that identify those individuals. Or, as I mentioned earlier, people have varying thresholds. You may want to recruit as your first adopters those people with low thresholds. Those are five different options, or tactics that you might choose. There may be even more that I haven’t thought of yet for identifying key individuals to be change agents. 

For leaders, it turns out that this is the most difficult network intervention. It is easy to measure and do, it’s intuitively appealing, people have been doing this for well over twenty years now and in every randomized controlled trial or field experiment, identifying opinion leaders via network analysis and using them as champions has proven to be effective both clinically and substantially among populations. In fact, we did a review paper in 2007 in which we identified over twenty studies that had used network data to identify opinion leaders and hundreds of other studies that have used other opinion leader identification techniques. Now, what surprised me about this review when we did it, my expectation was that there would be a handful, maybe no more than two or three studies, that had used network data to identify opinion leaders to bring about change. It turns out that there were a lot, however, they were distributed in different areas. That is, they were studies that were done among different disease states for different organs. So there was very little overlap between the studies, so in essence, people in different domains, different clinical domains, were rediscovering the effectiveness of opinion leaders over and over again. I can tell you anecdotally that this is also something that is done a lot on the fly. People do not necessarily collect network data, nor do they bother to publish their findings, they just do it in order to make for more effective organizational change and that’s all they are interested in. They don’t need to publish and get promoted and get tenure the way I do. 

We ran some simulations which show in fact that if you start diffusion processes with opinion leaders relative to starting the process with randomly selected individuals for those on the margin, you’ll get much faster uptake in diffusion of the innovation. The process to do this working with opinion leaders, is simply to identify them, recruit them, but you may have to convert them. In other words, sometimes you identify opinion leaders who may be resistant or not on board with the behavior change you want to bring, and so you are going to have to go through a process of converting them to get them to be your message bearers. 

I think it’s important to consider this because my feeling is that if you identify opinion leaders that are not on board for your behavior change program, that tells you something very important about the setting that you’re working in. If the opinion leaders are not going to support it, it is very unlikely you are going to be able to get uptake in the community regardless of what you do unless you get those opinion leaders on board.  In other words, people that are central in the network can act as very efficient barriers to your behavior change process. Even if you do not use the network data to try to develop a network intervention, it is very important diagnostic information to have for your intervention plans. For example, if you discover with the network data that the opinion leaders are already on board, you may realize that you are not going to have a difficult challenge in front of you in those communities. 

This is just a graph of a social network. I conduct a lot of research among adolescents and schools. We ask them who their friends are, they give us this information, and they do it quite quickly. In fact, students love giving us this information because it is one of the few things that they are actually certain of, they know who their friends are. Many of the other complicated attitudinal or behavioral questions we ask them can stump them a little bit, but once we ask who your friends are, typically, we give the students a roster with ID numbers and they simply write the ID numbers down. It makes it easy for us to enter the data. This is a typical kind of network signature that you might see. Interestingly enough, in this case, these are twelve-year-old sixth grade students, and they cluster into two different groups and it turns out that there is homophile here. Not surprisingly, boys nominate other boys as their friends, and girls nominate other girls as their friends. As the parent of a daughter and a son, I am delighted to see this; unfortunately, this picture gets quite fuzzy after a couple of years when boys and girls start to interact with one another on a more regular basis. 

From these data we can identify who the central modes are, twenty-one... two... and a couple of others... three... is very central, those who receive a lot of nominations and we can also see who those bridging nodes are; those that are connected to the other community. We can do a sub group analysis to find these sub groups. We can find people that are on the periphery and group members and isolate some [inaudible]. Network analysts love to get this data and we do all kinds of manipulations on it. Most of the people in the International Network for Social Network Analysis, which is the professional association of network people who like to network with one another and do networking, are happy to get this data and start applying all kinds of mathematical and analytic approaches to it.

Now, I have just talked briefly here about identification of individuals and leaders. We can do that in a number of different ways. Let me now talk about segmentation approaches. In segmentation approaches, we typically try to turn the network into not a whole network, but instead find out where the sub groups are. Segmentation interventions typically identify the group and deliver the intervention at the group level. This can be particularly advantageous. For example, let’s say you want to start an intervention. Instead of trying to change the whole organization, working with the sub group will enable you to find a group that can start working with one another, change each other’s behavior, reinforce each other’s behavior change, and you can learn from that process before you move on to other sub groups in the organization or in the community. Now, groups are set for people who are nodes that are definitely connected to one another. Our working definition of a group is those people that are more connected to one another than they are to the rest of the network as a whole. The network analytic community has dozens, perhaps more at this stage, of algorithms that identify groups, cliques, communities, and so on in the network. There are many behaviors for which change only makes sense if the entire group does it. For example, interactive technology is an example. Facebook is only worthwhile when all of your friends are also on Facebook. Right? The first person who got a fax machine couldn’t really use it for very much until they had other people to fax to; so many interventions really are only worthwhile when the whole group adopts them, so it’s important to identify those groups a priori to starting your intervention. 

Here is another network... we can see that we have a blue group, a red group, and then another group down here that is separate from this red group. So in identifying these sub groups if I was working in an organization, then you’d say, well let me work with this group first; make sure I can get them to adopt the innovation and share their experiences with each other before I move to the next group and then the next group. 

The third category is induction. In induction, we are using the network structure to try to accelerate behavior change, so the most prominent example of induction that I have worked with in the notion of matching leaders to the groups within which they exist. For example, we think of leader identification techniques and identifying leaders, converting them, and then turning them loose. Well, in fact, leaders do not lead for everybody. Leaders typically lead for those people who nominate them as their leaders, or nominate them as people they go to for advice. In the induction technique, we want to take that information, identify the leaders and then match those leaders to the people who have nominated them as leaders. This builds on the naturally occurring network, it will emphasize homophile between leaders and their followers, and we know that homophilous communications are more effective and can be more persuasive. The evidence seems to show that leaders are more effective if they are assigned to lead for the people who nominate them. I like to use this graph as an example. Suppose we are trying to change the behavior of person number twenty-two. If we identify a leader like number two over here, two is very far removed from twenty-two, so two is not likely to have an influence on person twenty-two. Three and two influence one another and they are good leader-follower pairs; but if I want to find a leader to influence twenty-two, it’s going to be person eighteen who twenty-two has nominated as somebody that they are connected to. So we have this nice little diagram, here is a small network, we identified the leaders, and we matched those leaders to the person that nominated them. 

Let me just mention that I knew we wouldn’t have time to go over this, but we have had the opportunity to conduct two randomized controlled trials in the case of adolescent behavior, adolescent smoking and substance use, and shown that using groups defined on this induction network characteristic was more effective than constructing groups randomly or just identifying the leaders. So it turns out that there are some avenues that this induction technique is a more effective way to bring about behavior change. 

To illustrate all of the different tactics that we have in the network intervention tool kit, I have created a hypothetical network like this. Then I started to run....I’ve written an R-script that’s freely available that you can use to calculate all of these network interventions and then as part of that process, I printed graphs to illustrate what the different tactics look like, how they identify different individuals, or what not. For example, this graph highlights the nodes that would be identified as those having the highest degree that we would use as opinion leaders. You see again, this kind of structural equivalence problem. The nodes that have highest closeness, the ones that can reach everybody else in a few steps, are these nodes. Again, you see that they do not span the network quite as efficiently as we would like. The nodes of highest between-ness are these here; they do span the network quite a bit and occupy very critical positions in the overall structure of the network. 

The Borgatti key-player algorithm identified now four leaders that seem to span the network much more optimally than just relying on the centrality measures as we have done before. The key player negative algorithm finds slightly different individuals. These ones, when removed, would fragment the network the most. We have an algorithm for identifying brokers in the network and here they are, we have an algorithm for identifying bridges in the network; they also seem to do a pretty good job spanning the network the way the key player negative algorithm does. We can identify high threshold and low threshold adopters, we can find sub groups in the network, I’ve differentially colored the subgroups. We can find different positions in the network, those people that are similar based on the structural equivalence to who they are directly connected to in the network, and we can look at hierarchy. Here I have simply graphed the network by their network connectivity. Person eight and twenty-eight have the most ties, then there’s another level in the hierarchy and another and another and so forth. 

That would seem to be important for many human service interventions in which we have an organization that is characterized by directors, program managers, line staff and so on. Obviously, you probably want different interventions for different levels of the hierarchy. 

Word of mouth diffusion here, I just illustrated what would happen if I selected some leaders and they were then to spread their message along the network. We have snowball interventions where we select seeds, index individuals and they recruit others, we have outreach interventions where people bring others they are connected to into an intervention, and we have the induction-matching algorithm illustrated here. We have measures for vitality, so who are the nodes most critical for the network when if removed they would disrupt the network the most. We can have node addition interventions. Here we are talking about the last strategy, network alterations, which I did not discuss very much; but we have mathematical algorithms we can apply to figure out what happens when you add nodes to the network, where should you add them, what happens when you want to delete links in the network, which of those links that are most profitable to delete from the network, which are the links that would be most profitable to add to the network, and here are the red links that when added would increase network cohesion the most. 

Finally, we can rewire networks. We can make networks more centralized, decentralized, more cohesive, and more fragmented. I have rewired these networks to be small world networks. We think small world networks are very efficient for spreading ideas on, and so one thing you might want to do in your organization is to change your network, make it a small world network. Or, you rewire the network on behavior. Sometimes we want to make sure that non-adopters can be linked with adopters, a so-called buddy system, and so we have written procedures for making that kind of match. 

In sum, I put all these pictures on one slide just to show you we have lots of different network intervention choices. Many arrows in the quiver, if you will, and we now need to give some thought to what are the principles or rules that might direct us towards choosing one network intervention over another. 

Well, you have them thinking about this, we don’t think we know very much about it. It occurs to me that we base our selection of a network intervention in part based on the type of network data that we have; and even the existing network structure. It may be that we have no network at all and we need to build one; or it may be that what matters a lot are behavioral characteristics. What is the existing prevalence? What is the distribution of that prevalence by network position; and what are the various perceived characteristics of the attribute that we are working with? Is it culturally compatible? What are the costs? Is it [inaudible]? 

There is one area that I started to work on lately which I think is interesting because we now know from a fair amount of social science over a few decades that we have several theoretical mechanisms that drive contagion and behavior change and I talked about many of those at the beginning of this presentation when I was articulating different sources of social influence. It may be that if we have particular mechanisms that are driving the behavior in certain settings, then we want to choose our network intervention strategy or tactic based on that theoretical mechanism. This is purely speculative, but I have built a chart of the different tactics that we have discussed so far, that we have articulated as being available for network intervention. Well, if this theoretical mechanism that’s driving the behavior in the community, then that may dictate, or at least inform the kind of network intervention you want to choose. It is a guide... it is a possibility. I do not know if its right, it could be completely wrong, but it is a thought. 

Okay, and then I just want to touch on a couple of final points before we close and one is that in network analysis... I have oftentimes been talking to you about one network. In fact, we usually measure multiple networks. In particular, there are two kinds of networks we measure quite often to understand social influence. One is expertise, how knowledgeable the person is on the behavior, how much do they know and how expert are they? Then there is also trust. How much do you trust the person? How much can you talk to them? People who are high in trust are often “people” people... trust networks tend to be more symmetric, dialogic and so on, and they are closer geographically. Expertise networks are often times further away geographically and they are maybe less personal. Of course, we want to find leaders, we want to find champions that fall into this high-high category, but if the barriers to adoption are ones of trust, it may be just as efficient to find leaders who are  high in trust, expertise may be less important than conversely for the expertise dimension. So that’s just something to consider and we oftentimes measure multiple networks and those multiple networks tell us a lot about the community we are working with. 

A quick example from a community study, these are physicians. You can see we measured discussion and advice. The gold links are advice, the blue ones are discussion, and as you can see, there is a lot of distinctiveness among these ties. In other words, people are quite consistent. There are quite a lot of them reporting different individuals they discuss things with rather than those they go to for advice. We can oftentimes measure leadership, too. This is a prominence network, and you see that its network signature is quite different from the discussion and advice network. 

Now I am going to prattle on for a couple more minutes, but I would like to give you all the opportunity, now that you have learned about social network analysis, how wonderful and beautiful it is. I would like to ask a poll question on whether or not you would be interested in designing a network intervention in your community or organization. Obviously, it is something that I am trying to devote a fair amount of energy to. I am hoping many people say yes, and that I can provide advice on how to do that. It is my firm belief that we can learn a lot about communities. We can learn a lot about social science and behavior change by conducting network interventions. It is something that has been neglected for a long time and is now becoming available because of the advent of new computing technology, which makes network data quite available. It is becoming popular because Twitter, Facebook, Pinterest, and many other online systems are available. I do have to spend a lot of time these days explaining to people that I am not a Facebook person. Just because I am a social network analyst doesn’t mean that I spend all of my time on Facebook. In fact, I don’t and I was pretty much a low threshold adopter for Facebook as well, but yeah, with e-mail communications and what not, there are a lot of opportunities to have unobtrusive measures of networks and collect that data. 

Here are the poll results. Over fifty percent... fifty-six percent it looks like people that would be interested in conducting network interventions. Thirty-three percent say help me. I hope I can be of assistance. I will be more than happy to be of assistance as I can, because as I said, this is sort of my goal, and on a personal note, no, no, no, do not leave...

Speaker:It looks like we just lost Tom. I am not sure what happened there. To our audience, I apologize, hopefully... I am sure he just lost his network connection, and hopefully he will get back in shortly. I apologize. Hopefully this is not a long wait. 

Brian Mittman:He was speaking through a USB mic? 

Speaker:Yes, he was, but he completely dropped off the meeting, so I am assuming that his network connection dropped him off. We just got a comment in from the audience, ironic. Yes, it kind of is, but yeah because his connection to the meeting was severed because he was using mic and speaker, we lost both. He is... it looks like he just rejoined; hopefully we will have his audio back in just a second here. 

Tom Valente:Am I back? 

Speaker:You are back, yeah. Thank you. 

[Laughter] 

Tom Valente:I was doing so well. I was almost proud of myself. 

Speaker:These things happen. 

Tom Valente:They do. Okay, fortunately we are at the end, just a couple concluding sides: there are theories, methods out there that can be used for interventions. I think they have promise for improving health outcomes. I am an avid public health person and think it is important that we do the best job we can to try to improve the conditions under which people live and work. It is also an opportunity to do interesting theoretical work and frankly, this is really a hot topic now. When I started doing this work, of course, you could not get any funding for doing social network research. Now there seems to be opportunities for it in many places. I think in part because it is showing some utility and that the evidence is showing it is not just a fancy theory, it helps us say some interesting things about people. It is also something that we can use to improve people’s condition. 

I want to just underscore this notion that... well, it’s coming up to Christmas time, you may have many academic friends that would be interested in either the print version or the Kindle version of either of these lovely books, which explain all the details about social network analysis and how to get started, so don’t hesitate. You can go to amazon.com and you will find a nice discounted price on both of these books, which would be, I think a lovely addition to anyone’s stocking. 

That’s it, I encourage your questions and I encourage, if I can be of assistance, to please feel free to reach out to me and my e-mail address tvalente@usc.edu, please feel free to use that and I hope we have some time for questions. I know I used a lot of it, so I am not sure what happens next. 

Moderator:I do have some pending questions here. We technically have three minutes until we finish, and I know a lot of people just have an hour for the session, but we will try to get through what we have here as quickly as we possibly can. For our audience, if you do have a question, feel free to send it in using the Q and A screen and we will do our best to deal with the questions as best we can. 

The first question we have here: Are there opinion leaders who do not self-identify as opinion leaders? 

Tom Valente:Yes there are. In fact, there is an interesting thing that happens when we use network data and we find opinion leaders, many say oh yes, I knew I was an opinion leader and are happy to be identified that way because we can give them resources that they can use. On the other hand, some people are surprised, although it’s few, but some are surprised. When we use self-identification and a sociometric, we see not too much overlap. In fact, there is a good portion of people who self-identify as opinion leaders, who are not considered opinion leaders by their peers. 

Moderator:Thank you, the next question: Where might we be able to access that R script? Is it in the standard packages accessible from C-Ren or through Dr. Valente’s personal site/ftp? 

Tom Valente:You would have to e-mail me and I will e-mail it to you. The code was written... the article that I published in Science; it was included as an online supplement to the article. I do not know if that is free to access. If not, then certainly e-mail me and I will send you the R-script. 

Moderator:Thank you. The next question: It seems like trust and experience are criteria for identification of leaders. What other tools or criteria are used to determine who are opinion leaders or key players? 

Tom Valente:A great question; there are lots of different networks you can measure. Who do you go to for lunch? Who do you want to have a romantic relationship with and so on and so forth. We have had the most success at understanding, at measuring expertise and discussion, trust and experience being the two critical ones. The other one that we have used, but I don’t recommend for interventions is who do you consider to be a leader because I think it identifies not somebody that you would necessarily feel leads for you, but instead who you think the community identifies as a leader. So that’s been a third criteria that’s been used. Those are the three primary ones. 

Moderator:Thank you. The next question, I am not sure I understand it; I will read it the way that it was submitted here. Is leadership slash influence expertise dimension network measure perceived expertise... perceived expertise at communicating slash-sharing expertise? 

Tom Valente:Yeah... that is a good question, so it is perceived expertise. In other words, you may have people who are perceived to be very knowledgeable about something, but in fact, they are not, and they just think that they are, or other people think they are. It reminds me of when I was first teaching and I had a mentor tell me, well students are not very good evaluators of your teaching because they do not know the subject that you are teaching only your colleagues do. It is true that we are measuring perceived expertise with the network question, not actual expertise. Of course, that would be an interesting research question to determine how well those two things align. We oftentimes think that crowd sourcing does a very good job of identifying who people are that are high in expertise, but there could be some stakes there and it could be important to make sure people who are identified as high in expertise, are in fact experts in the subject that you’re talking about. 

Moderator:Thank you and I know we are one minute past the top of the hour, but I just have one final question if we can sneak that in here. The question is: What is the funding mechanism supporting social network studies? Is NIH interested? 

Tom Valente:NIH is interested and in fact NIH has an existing call out from the Office of Behavioral and Social Science Research on social networks and health and they fund a few RO1’s each year through that mechanism, which is just specifically about social network analysis and social network theory. The individual institutes are also funding many network studies, so NIDA funds research on substance abuse and social networks work on injection drug users and community based network research. NICHD is funding work in this area, USAID funds work to understand how we get development programs implemented, the National Science Foundation has programs for network analysis and the individual private foundations are also interested in this. Really, it’s a situation of an embarrassment of riches for somebody like myself for the first time. For the first fifteen years or so, there was no funding available. Now it seems like pretty much everybody is interested in it. So, yes, NIH is keen on it, and the one thing that we are lacking a little bit is making sure that the review panels have the necessary expertise in social network analysis. Still, those people who are trained in individual approaches to behavior change without a lot of attention to the importance of social context. That is one barrier that we do face a little bit, but otherwise I’ve seen a dramatic increase in the funding from federal agencies. 

Moderator:Great, thank you and that does wrap up all of our questions. Dr. Valente, I really want to thank you for taking the time to present for us today. We really very much appreciate the time that you have put into preparing and presenting for us today. For our audience, thank you very much for joining us today, and bearing with us through the unfortunate quick technical issue. I am glad we were able to get that ironed out quickly; and just to let our audience know, as you leave the session today, you should have a feedback survey pop up on your screen. We would very much appreciate if you would take a few moments to fill that out; we definitely do look at your feedback for our current and future planning of cyber seminars. Thank you everyone for joining us for today’s HSR&D cyber seminar, we hope to see you at a future session. Thank you. 

Tom Valente:Thank you all very much for listening in. I appreciate it. 

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